On Graph Theory: On Graphs (-1999)

"As a simple trick, the discrete can often be carried over into the continuous, in a way suitable for practical purposes, by making a graph of the discrete, with the values shown as separate points. It is then easy to see the form that the changes will take if the points were to become infinitely numerous and close together." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"The concepts a person uses are represented as points, and the causal links between these concepts are represented as arrows between these points. This gives a pictorial representation of the causal assertions of a person as a graph of points and arrows. This kind of representation of assertions as a graph will be called a cognitive map. The policy alternatives, all of the various causes and effects, the goals, and the ultimate utility of the decision maker can all be thought of as concept variables, and represented as points in the cognitive map. The real power of this approach ap pears when a cognitive map is pictured in graph form; it is then relatively easy to see how each of the concepts and causal relation ships relate to each other, and to see the overall structure of the whole set of portrayed assertions." (Robert Axelrod, "The Cognitive Mapping Approach to Decision Making" [in "Structure of Decision: The Cognitive Maps of Political Elites"], 1976)

"A semantic network or net represents knowledge as a net-like graph. An idea, event, situation or object almost always has a composite structure; this is represented in a semantic network by a corresponding structure of nodes (drawn as circles or boxes) representing conceptual units, and directed links (drawn as arrows between the nodes) representing the relations between the units. […] An abstract (graph-theoretic) network can be diagrammed, defined mathematically, programmed in a computer, or hard-wired electronically. It becomes semantic when you assign a meaning to each node and link. Unlike specialized networks and diagrams, semantic networks aim to represent any kind of knowledge which can be described in natural language. A semantic network system includes not only the explicitly stored net structure but also methods for automatically deriving from that a much larger structure or body of implied knowledge." (Fritz Lehman, "Semantic Networks",  Computers & Mathematics with Applications Vol. 23 (2-5), 1992)

"The essential idea of semantic networks is that the graph-theoretic structure of relations and. abstractions can be used for inference as well as understanding. […] A semantic network is a discrete structure as is any linguistic description. Representation of the continuous 'outside world' with such a structure is necessarily incomplete, and requires decisions as to which information is kept and which is lost." (Fritz Lehman, "Semantic Networks",  Computers & Mathematics with Applications Vol. 23 (2-5), 1992)

"The idea of a planar graph, and other concepts which spring from that, bring together two areas of mathematics, graph theory (which is essentially the study of some relations) and the geometry of surfaces (which is part of topology). As often happens in mathematics, the marriage of two different disciplines results in some interesting (and sometimes rather difficult) offspring." (Victor Bryant, "Aspects of Combinatorics: S wide-ranging introduction", 1993)

"A graph is a good way to mathematically represent a physical situation in which there is a flow of something - materials, people, money, information – from one place to another." (John L Casti, "Five Golden Rules: Great Theories of 20th-Century Mathematics - and Why They Matter", 1995)

"Graph theory is typical of much modern mathematics. Its subject matter is not traditional, and it is not a development from traditional theories. Its applications are not traditional either. […] Graph theory is not concerned with continuous quantities. It often involves counting, but in integers, not measuring using fractions. Graph theory is an example of discrete mathematics. Graphs are put together in pieces, in chunks, rather like Meccano or Lego, or a jigsaw puzzle." (David Wells, "You Are a Mathematician: A wise and witty introduction to the joy of numbers", 1995)

"Depth First Search is especially appropriate for threading mazes, because it is possible to use it without having a map of the maze. It involves only local rules at nodes, plus a record of nodes and edges already used, so you can explore the graph and traverse it as you go. The name indicates the basic idea: give top priority to pushing deeper into the maze. The number of steps required is at most twice the number of passages in the maze." (Ian Stewart, "The Magical Maze: Seeing the world through mathematical eyes", 1997)

"Graphs are one of the unifying themes of computer science - an abstract representation that describes the organization of transportation systems, human interactions, and telecommunication networks. That so many different structures can be modeled using a single formalism is a source of great power to the educated programmer." (Steven S Skiena, "The Algorithm Design Manual", 1997)

"One very effective approach is to represent all the possible actions as a maze, and try to find a route through it. It is a logical maze rather than a real one, touched with that magic genius of mathematical transformations in which a problem that seems unassailable in one form becomes trivial in another, logically equivalent one. The idea is to represent the problem in a visual manner, using a diagram called a graph. A graph consists of a number of nodes (dots) linked by edges (lines), possibly with arrows on them. Each 'state' of the puzzle - position of the items of produce relative to the river - is represented by a node. Each 'legal' move between states is represented as an edge joining the corresponding nodes. If necessary, arrows can be added to the edges to show which is the starting state and which is the end state. The solution of the puzzle then reduces to tracing a path through its graph, starting from the initial state of the problem and finishing at the desired final state. The graph is a kind of conceptual map of the puzzle - a maze of possible states whose passages are the edges of the graph and whose junctions are its nodes." (Ian Stewart, "The Magical Maze: Seeing the World Through Mathematical Eyes", 1997)

"The three basic mechanisms of averaging, feedback and division of labor give us a first idea of a how a CMM [Collective Mental Map] can be developed in the most efficient way, that is, how a given number of individuals can achieve a maximum of collective problem-solving competence. A collective mental map is developed basically by superposing a number of individual mental maps. There must be sufficient diversity among these individual maps to cover an as large as possible domain, yet sufficient redundancy so that the overlap between maps is large enough to make the resulting graph fully connected, and so that each preference in the map is the superposition of a number of individual preferences that is large enough to cancel out individual fluctuations. The best way to quickly expand and improve the map and fill in gaps is to use a positive feedback that encourages individuals to use high preference paths discovered by others, yet is not so strong that it discourages the exploration of new paths." (Francis Heylighen, "Collective Intelligence and its Implementation on the Web", 1999)

On Graph Theory: On Graphs (2000-2012)

"Euler's proof that in Konigsberg there is no path crossing all seven bridges only once was based on a simple observation. Nodes with an odd number of links must be either the starting or the end point of the journey. A continuous path that goes through all the bridges can have only one starting and one end point. Thus, such a path cannot exist on a graph that has more than two nodes with an odd number of links. As the Konigsberg graph had four such nodes, one could not find the desired path." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"Regular graphs are unique in that each node has exactly the same number of links. […] Such regularity is clearly absent from random graphs. The premise of the random network model is deeply egalitarian: We place the links completely randomly; thus all nodes have the same chance of getting one […] If the network is large, despite the links' completely random placement, almost all nodes will have approximately the same number of links." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"'There is an old debate', Erdos liked to say, 'about whether you create mathematics or just discover it. In other words, are the truths already there, even if we don't yet know them?' Erdos had a clear answer to this question: Mathematical truths are there among the list of absolute truths, and we just rediscover them. Random graph theory, so elegant and simple, seemed to him to belong to the eternal truths. Yet today we know that random networks played little role in assembling our universe. Instead, nature resorted to a few fundamental laws, which will be revealed in the coming chapters. Erdos himself created mathematical truths and an alternative view of our world by developing random graph theory." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"Undirected graphs are an alternative to directed graphs for representing independence relations. Since both directed and undirected graphs are used in practice, it is a good idea to be facile with both. The main difference between the two is that the rules for reading independence relations from the graph are different." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"In modern terminology, a collection of points in space (called vertices) and lines (called edges) joining selected pairs of those points is called a graph, and the study of graphs is called graph theory. A graph that can be drawn on the plane so that the joining lines intersect only at vertices is called a planar graph." (David Gay, "Explorations in Topology", 2007)

"The important graphs are the ones where some things are not connected to some other things. When the unenlightened ones try to be profound, they draw endless verbal comparisons between this topic, and that topic, which is like this, which is like that; until their graph is fully connected and also totally useless." (Eliezer S Yudkowsky,  "Mysterious Answers to Mysterious Questions", 2007)

"The general concept of a map is not so different from that of a model, though the one is extrapolated from a graph with spatial similarity to certain features of a landscape, and the other from a table-top contraption."  (Bas C van Fraassen, "Scientific Representation: Paradoxes of Perspective", 2008)

"A first step in understanding complex systems is trying to understand patterns and regularities of interactions in a way which might make it possible to break the systems down into possible subcomponents. To do so, it is necessary to find a way of representing complex systems. […] A convenient way to represent complex systems is through graphs or networks." (Jörg Reichardt, "Structure in Complex Networks", 2009)

"A graph in mathematics is a set of nodes and a set of edges between pairs of those nodes; the edges are ordered or nonordered pairs, or a relation, that defines the pairs of nodes for which the relation being examined is valid. […] The edges can either be undirected or directed; directed edges depict a relation that requires the nodes to be ordered while an undirected edge defines a relation in which no ordering of the edges is implied." (Dennis M Buede, "The Engineering Design of Systems: Models and methods", 2009)

"A graph enables us to visualize a relation over a set, which makes the characteristics of relations such as transitivity and symmetry easier to understand. […] Notions such as paths and cycles are key to understanding the more complex and powerful concepts of graph theory. There are many degrees of connectedness that apply to a graph; understanding these types of connectedness enables the engineer to understand the basic properties that can be defined for the graph representing some aspect of his or her system. The concepts of adjacency and reachability are the first steps to understanding the ability of an allocated architecture of a system to execute properly." (Dennis M Buede, "The Engineering Design of Systems: Models and methods", 2009)

"Democracy is a highly robust, but at the same time very fragile, complex system, which requires constant change to maintain its self-organized state. Democracy is by definition not in equilibrium. The random graph pattern may pose a system of boring and very low-complexity equilibrium. If all of us ever find ourselves with plenty of resources, that will be THE END of our history." (Péter Csermely, "Weak Links: The Universal Key to the Stabilityof Networks and Complex Systems", 2009)

"For the study of the topology of the interactions of a complex system it is of central importance to have proper random null models of networks, i.e., models of how a graph arises from a random process. Such models are needed for comparison with real world data. When analyzing the structure of real world networks, the null hypothesis shall always be that the link structure is due to chance alone. This null hypothesis may only be rejected if the link structure found differs significantly from an expectation value obtained from a random model. Any deviation from the random null model must be explained by non-random processes." (Jörg Reichardt, "Structure in Complex Networks", 2009)

On Graph Theory: On Graphs (2010-2019)

"First, what are the 'graphs' studied in graph theory? They are not graphs of functions as studied in calculus and analytic geometry. They are (usually finite) structures consisting of vertices and edges. As in geometry, we can think of vertices as points (but they are denoted by thick dots in diagrams) and of edges as arcs connecting pairs of distinct vertices. The positions of the vertices and the shapes of the edges are irrelevant: the graph is completely specified by saying which vertices are connected by edges. A common convention is that at most one edge connects a given pair of vertices, so a graph is essentially just a pair of sets: a set of objects." (John Stillwell, "Mathematics and Its History", 2010)

"The difference between necessary and sufficient conditions seems an obvious one, yet they are surprisingly often confused in mathematical proofs. Formally, in graph theory, conditions are used to prove properties of graphs. When a condition C is said to be necessary, this means that a property P can hold only if C is met. When a condition C is said to be sufficient, this means that if C is met, then property P will hold true. And indeed, when property P is true if and only if condition C is met, indicates that C is a necessary and sufficient condition for property P to be valid." (Maarten van Steen, "Graph Theory and Complex Networks: An Introduction", 2010)

"Graphs are among the most important abstract data structures in computer science, and the algorithms that operate on them are critical to modern life. Graphs have been shown to be powerful tools for modeling complex problems because of their simplicity and generality." (Jeremy Kepner & John Gilbert [Eds],"Graph Algorithms in the Language of Linear Algebra", 2011)

"A network (or graph) consists of a set of nodes (or vertices, actors) and a set of edges (or links, ties) that connect those nodes. [...] The size of a network is characterized by the numbers of nodes and edges in it." (Hiroki Sayama, "Introduction to the Modeling and Analysis of Complex Systems", 2015)

"Network theory confirms the view that information can take on 'a life of its own'. In the yeast network my colleagues found that 40 per cent of node pairs that are correlated via information transfer are not in fact physically connected; there is no direct chemical interaction. Conversely, about 35 per cent of node pairs transfer no information between them even though they are causally connected via a 'chemical wire' (edge). Patterns of information traversing the system may appear to be flowing down the 'wires' (along the edges of the graph) even when they are not. For some reason, 'correlation without causation' seems to be amplified in the biological case relative to random networks." (Paul Davies, "The Demon in the Machine: How Hidden Webs of Information Are Solving the Mystery of Life", 2019)

"We wrote down all the states and legal moves (here it turned out to be helpful to have a systematic notation, but that's not essential). Then we formed a graph whose nodes correspond to states and whose edges correspond to legal moves. The solution of the puzzle is then a path through the graph that joins the start to the finish. Such a path is usually obvious to the eye, provided the puzzle is sufficiently simple for the entire graph to be drawn. Puzzles of this type are really mazes, for a maze is just a graph drawn in a slightly different fashion. Metaphorically, they are logical mazes - you have to find the right sequence of moves to solve them. The graph turns the logical maze into a genuine maze, turning the metaphor into reality. The fact that solving the real maze also solves the logical maze is one of the magical features of the maze that is mathematics." (Ian Stewart, "The Magical Maze: Seeing the World Through Mathematical Eyes", 1997)

On Graph Theory: On Graphs (2020-2029)

"A graph is a simple and quite old mathematical concept: a data structure consisting of a set of vertices (or nodes/points) and edges (or relationships/lines) that can be used to model relationships among a collection of objects." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"[...] graphs are extremely useful for encoding information, and data in graph format is increasingly plentiful. In many areas of machine learning - including natural language processing, computer vision, and recommendations - graphs are used to model local relationships between isolated data items (users, items, events, and so on) and to construct global structures from local information. Representing data as graphs is often a necessary step (and at other times only a desirable one) in dealing with problems arising from applications in machine learning or data mining." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs, with multiple node types and different types of relationships, are far from being a Euclidean space. This task is where graph neural networks (GNNs) comes in. GNNs are deep learning-based methods that operate on a graph domain to perform complex tasks such as node classification (the bot example), link prediction (the disease example), and so on. Due to its convincing performance, GNN has become a widely applied graph analysis method." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"In many areas of machine learning, graphs are used to model local relationships between data elements and to build global structures from local information. Building graphs is sometimes necessary for dealing with problems arising from applications in machine learning or data mining, and at other times, it's helpful for managing data. It's important to note that the transformation from the original data to a graph data representation can always be performed in a lossless manner. The opposite is not always true." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Several areas of graph theory are concerned with the likelihood or certainty of the presence in a graph of various subgraphs or, more generally, of graph properties that emerge as the number of vertices and/or the number of edges increases. Collectively they are grouped as analytic graph theory." (Jonathan L Gross et al, "Topics in Graph Theory", 2023)

"Some connected graphs are 'more connected' than others. That is, a connected graph’s vulnerability to disconnection by edge- or vertex-deletion varies. Two numerical parameters, vertex-connectivity and edge-connectivity, are useful in measuring a graph’s connectedness. Intuitively, a network’s vulnerability should be closely related also to the number of  alternative paths between each pair of nodes. There is a rich body of mathematical results concerning this relationship, many of which are variations of a classical result of Menger, and some of these extend well beyond graph theory." (Jonathan L Gross et al, "Topics in Graph Theory", 2023)

"Spanning trees capture the connectedness of a graph in the most efficient way, and they provide a foundation for a systematic analysis of the cycle structure of a graph. Mathematicians regard the algebraic structures underlying the collection of cycles and edge-cuts of agraph as beautiful in their own right. Establishing connections between linear algebra and graph theory provides some powerful analytical tools for understanding a graph’s structure." (Jonathan L Gross et al, "Topics in Graph Theory", 2023)

"[...] a graph is a mathematical model that is used for describing relationships between entities. However, each complex network presents intrinsic properties. Such properties can be measured by particular metrics, and each measure may characterize one or several local and global aspects of the graph." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"A point of caution when dealing with projections: be aware of the dimension of the projected graph.  In certain cases, such as the one we are considering here, projection may create extremely large numbers of edges, which makes the graph hard to be analyzed." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Most of the complexity indeed arises from the presence of entities that appear only once or very few times, but still generate cliques within the graph. Such entities are not very informative to capture patterns and provide insights. Besides, they are possibly strongly affected by statistical variability. On the other hand, we should focus on strong correlations that are supported by larger occurrences and provide more reliable statistical results." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"The concept of temporal graphs is useful in all the real-world problems that can be represented as a graph, where the nodes and edges of the graph may change over time. For example, temporal graphs are extensively applied in modeling social networks. By capturing the evolving relationships between individuals, temporal graphs enable a more accurate representation of social dynamics. This is particularly useful for predicting changes in friendships, community structures, and the information diffusion over time." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

Alessandro Negro - Collected Quotes

"A graph is a simple and quite old mathematical concept: a data structure consisting of a set of vertices (or nodes/points) and edges (or relationships/lines) that can be used to model relationships among a collection of objects." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"A widely adopted technique for solving the data sparsity issue and the cold-start problem is based on graph representation, navigation, and processing. Graph navigation methods (like the pathfinding example [...]) and graph algorithms (such as PageRank) are applied to fill some gaps and create a denser representation of [a] dataset."

"GNNs are capable of generating representations of nodes that depend on the structure of the graph as well as on any feature information we have. These features could be nodes’ properties, relationship types, and relationship properties. That’s why GNNs could drive the final tasks to better results. These embeddings represent the input for tasks such as node classification, link prediction, and graph classification." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"[...] graphs are extremely useful for encoding information, and data in graph format is increasingly plentiful. In many areas of machine learning - including natural language processing, computer vision, and recommendations - graphs are used to model local relationships between isolated data items (users, items, events, and so on) and to construct global structures from local information. Representing data as graphs is often a necessary step (and at other times only a desirable one) in dealing with problems arising from applications in machine learning or data mining." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs are powerful structures useful not only for representing connected information, but also for supporting multiple types of analysis. Their simple data model, consisting of two basic concepts such as nodes and relationships, is flexible enough to store complex information. If you also store properties in nodes and relationships, it is possible to represent practically everything of any size." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs are useful for representing how things are either physically or logically linked in simple or complex structures. A graph in which we assign names and meanings to the edges and vertices becomes what is known as a network. In these cases, a graph is the mathematical model for describing a network, whereas a network is a set of relations between objects, which could include people, organizations, nations, items found in a Google search, brain cells, or electrical transformers." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs can be used to model and analyze the relationships between entities as well as their properties. This aspect brings an additional dimension of information that graph-powered machine learning can harness for prediction and categorization. The schema flexibility provided by graphs also allows different models to coexist in the same dataset." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs can support machine learning by doing what they do best: representing data in a way that is easily understandable and easily accessible. Graphs make all the necessary processes faster, more accurate, and much more effective. Moreover, graph algorithms are powerful tools for machine learning practitioners. Graph community detection algorithms can help identify groups of people, page rank can reveal the most relevant keywords in a text, and so on." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"Graphs, with multiple node types and different types of relationships, are far from being a Euclidean space. This task is where graph neural networks (GNNs) comes in. GNNs are deep learning-based methods that operate on a graph domain to perform complex tasks such as node classification (the bot example), link prediction (the disease example), and so on. Due to its convincing performance, GNN has become a widely applied graph analysis method." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

"In many areas of machine learning, graphs are used to model local relationships between data elements and to build global structures from local information. Building graphs is sometimes necessary for dealing with problems arising from applications in machine learning or data mining, and at other times, it's helpful for managing data. It's important to note that the transformation from the original data to a graph data representation can always be performed in a lossless manner. The opposite is not always true." (Alessandro Negro, "Graph-Powered Machine Learning", 2021)

James G Scott - Collected Quotes

"A model is a metaphor, a description of a system that helps us to reason more clearly. Like all metaphors, models are approximations, and will never account for every last detail. A useful mantra here is: all models are wrong, but some models are useful." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"[...] always remember that the construction of an ANOVA table is inherently sequential. For example, first we add the clutter variable, which remains in the model at every subsequent step; then we add the distance variable, which remains in the model at every subsequent step; and so forth. Thus the actual question being answered at each stage of an analysis of variance is: how much variation in the response can this new variable predict, in the context of what has already been predicted by other variables in the model? This point - the importance of context in interpreting an ANOVA table - is subtle, but important." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"An obvious question is: do bootstrapped confidence intervals satisfy the frequentist coverage property? If your sample is fairly representative of the population, then the answer is a qualified yes. That is, the bootstrapping procedure yields nominal X% intervals that cover the true value 'approximately' X% of the time. Moreover, as the size of the original sample gets bigger, the quality of the approximation gets better. Alas, it is necessary to appeal to some very advanced probability theory to put both of these claims on firm footing." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"At the core of the resampling approach to statistical inference lies a simple idea. Most of the time, we can’t feasibly take repeated samples of size n from the population, to see how our estimate changes from one sample to the next. But we can repeatedly take samples of size n from the sample itself, and apply our estimator afresh to each notional sample. The idea is that the variability of the estimates across all these samples can be used to approximate our estimator’s true sampling distribution. This process - pretending that our sample is the whole population, and taking repeated samples of size n with replacement from our original sample of size n - is called bootstrap resampling, or just bootstrapping" (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"By themselves, sums of squares are hard to interpret, because they are measured in squared units of the Y variable. But their ratios are highly meaningful. In fact, the ratio of PV to TV - or what fraction of the total variation has been predicted by the model - is one of the most frequently quoted summary measures in all of statistical modeling. This ratio is called the coefficient of determination, and is usually denoted by the symbol R2 [...] The correct interpretation of R2 sometimes trips people up, and is therefore worth repeating: it is the proportion of variance in the data that can be predicted using the statistical model in question." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"Good estimators are those that usually yield estimates close to the truth, with minimal variation. Therefore, we typically summarize a sampling distribution using its standard deviation, which we refer to as the standard error. In quoting the standard error of an estimator’s sampling distribution, you are saying: 'If I were to take repeated samples from the population and use this estimatorfor every sample, my estimate is typically off from the truth by about this much.' Notice again that this is a claim about a procedure, not a particular estimate. The bigger the standard error, the less stable the estimator across different samples, and the less you can trust the estimate for any particular sample." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"In fitting statistical models, we typically equate the trustworthiness of a procedure with its stability under the influence of luck, and we seek to measure the degree to which that procedure might have given a different answer if the forces of randomness had made the world look a bit different. Specifically, the question we seek to answer is: 'if our data set had been different merely due to chance, would our answer have been different, too?'" (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"Model-building requires much more than just technical knowledge of statistical ideas. It also requires care and judgment, and cannot be reduced to a flowchart, a table of formulas, or a tidy set of numerical summaries that wring every last drop of truth from a data set. There is almost never a single 'right' statistical model for some problem. But there are definitely such things as good models and bad models, and learning to tell the difference is important. Just remember: calling a model good or bad requires knowing both the tool and the task." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"[...] complexity sometimes comes at the expense of explanatory power. We must avoid building models calibrated so perfectly to past experience that they do not generalize to future cases." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"It is common to view a statistical model as nothing more than a recipe for calculating the fitted values, and to think that the residuals are just the errors made by this model. But we’ll have a richer picture if instead we view the residuals as part of the model. If you’ve ignored the variation in the residuals, then you really haven’t specified a complete forecast." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"Resampling won’t yield the true sampling distribution of an estimator, but it is often good enough for approximating the standard error (which you’ll remember is just the standard deviation of the sampling distribution). We use the term bootstrapped standard error for the standard deviation of the bootstrapped sampling distribution. The bootstrapped standard error is an estimate of the true standard error." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"Tables are almost always the best way to display categorical data sets with few classifying variables, for the simple reason that they convey a lot of information in a small space." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

"The residuals from a regression model are sometimes called 'errors'. This is especially true in experimental science, where measurements of some Y variable will be taken at different values of the X variable (called design points), and where noisy measurement instruments can introduce random errors into theobservations. But in many cases this interpretation of a residual as an error can be misleading. A regression model can still give a nonzero residual, even if there is no mistake in the measurement of the Y variable. It’s often far more illuminating to think of the residual as the part of the Y variable that it is left unpredicted by X." (James G Scott, "Statistical Modeling: A Gentle Introduction", 2017)

On Literature: On Revolutions (From Fiction to Science-Fiction)

"No revolution, no heresy is comfortable or easy. For it is a leap, it is a break in the smooth evolutionary curve, and a break is a wound, a pain. But the wound is necessary; most of mankind suffers from hereditary sleeping sickness, and victims of this sickness (entropy) must not be allowed to sleep, or it will be their final sleep, death."  (Yevgeny Zamiatin, "On Literature, Revolution, Entropy, and Other Matters", 1923)

"Revolution is everywhere, in everything. It is infinite. There is no final revolution, no final number. The social revolution is only one of an infinite number of numbers; the law of revolution is not a social law, but an immeasurably greater one. It is a cosmic, universal law - like the laws of the conservation of energy and of the dissipation of energy (entropy)." (Yevgeny Zamiatin, "On Literature, Revolution, Entropy, and Other Matters", 1923)

"The art of war is simplicity itself compared with the art of revolution. War follows well-defined principles, and analogies may be traced down through the centuries, whether the fighting is done with pilum and ballista, or with rocket and disintegrator; but each revolution is a law unto itself, a freak, a monstrosity, whose conditions may not be repeated." (Robert A Heinlein, "If This Goes On", 1940)

"Revolutions are not won by enlisting the masses. Revolution is a science only a few are competent to practice. It depends on correct organization and, above all, on communications." (Robert A Heinlein, "The Moon Is a Harsh Mistress", 1966)

"Only the individual, the person, had the power of moral choice - the power of change, the essential function of life. The Odonian society was conceived as a permanent revolution, and revolution begins in the thinking mind." (Ursula K. Le Guin, "The Dispossessed: An Ambiguous Utopia", 1974)

"You cannot buy the Revolution. You cannot make the Revolution. You can only be the Revolution. It is in your spirit, or it is nowhere." (Ursula K. Le Guin, "The Dispossessed: An Ambiguous Utopia", 1974)

"Revolutions begin in the universities; the streets breed only riots." (George Turner, "Drowning Towers", 1987)

"If these aging treatments work, and we are living decades longer than previously, it will certainly cause a social revolution. Shortness of life was a primary force in the permanence of institutions, strange though it is to say it." (Kim S Robinson, Red Mars (1992)

"Don’t put your trust in revolutions. They always come around again. That’s why they’re called revolutions. People die, and nothing changes." (Terry Pratchett, "Night Watch", 2002)

On Literature: On Mutation (From Fiction to Science-Fiction)

"It was a species which often considered itself to be, basically, a race of divinely inspired toolmakers; any intelligent entity from Arcturus would instantly have perceived them to be, basically, a race of impassioned after-dinner speech-makers." (Walter M Miller Jr, "A Canticle for Leibowitz", 1959)

"Species evolve to meet the environment. An intelligent species changes the environment to suit itself. As soon as a species becomes intelligent, it should stop evolving." (Larry Niven & Jerry Pournelle, "The Mote in God’s Eye", 1974)

"We are only the beginning of humanity, the larval stage, the species preparing for its discovery of what intelligence is for. We will survive and develop, each crest a little higher than the one before." (George Turner, "Drowning Towers", 1987)

"It is unusual for us to discover an imperial power-system in space. As a rule, such archaic forms of authority wither long before the relevant species drags itself off the home planet." (Iain M Banks, "The Player of Games", 1988)

"The great thing about being the only species that makes a distinction between right and wrong is that we can make up the rules for ourselves as we go along." (Douglas Adams, "Last Chance to See", 1990)

"Somewhere in its history, every technological species will make the tools to become godlike. Immortal citizens will be capable of building worlds, or obliterating them. How a species responds to the challenge... well, that’s what determines its fate, more often than not." (Robert Reed, "Sister Alice", 1993)

"It is a paradox of Life that all species breed past mere replacement. Any paradise of plenty soon fills to become paradise no more." (David Brin, "Brightness Reef", 1995)

"Mutation. It is the key to our evolution. It is how we have evolved from a single-celled organism into the dominant species on the planet. This process is slow, normally taking thousands and thousands of years. But every few hundred millennia, evolution leaps forward." (Michael Dougherty,% et al, "X2: X-Men United", [film] 2003)

"Science fiction, outside of poetry, is the only literary field which has no limits, no parameters whatsoever. You can go not only into the future, but into that wonderful place called "other", which is simply another universe, another planet, another species." (Theodore Sturgeon)

On Literature: On Asteroids, Comets & Meteors (From Fiction to Science Fiction)

"Above them Phileas Fogg moved in majestic indifference. He was following his own rational orbit around the world, without bothering at all about the asteroids gravitating around him." (Jules Verne, "Around the World in Eighty Days", 1873),

"Nothing can be prettier than to see the movement, in perfectly harmonic relations, of planets round their centres, of satellites around planets, of suns, with their planets and satellites, around their centres, and of these in turn around theirs. And to persons who have loved earth as much as I do, and who, while at school there, have studied other worlds and stars, then distant, as carefully as I have, nothing, as I say, can be more charming than to see at once all this play and interplay; to see comets passing from system to system, warming themselves now at one white sun, and then at a party-colored double; to see the people on them changing customs and costumes as they change their light, and to hear their quaint discussions as they justify the new and ridicule the old." (Edward E Hale, "Hands Off", 1881)

"There is a sense of spectral whirling through liquid gulfs of infinity, of dizzying rides through reeling universes on a comet’s tail, and of hysterical plunges from the pit to the moon and from the moon back again to the pit, all livened by a cachinnating chorus of the distorted, hilarious elder gods and the green, bat-winged mocking imps of Tartarus." (Howard P Lovecraft, "The Call of Cthulhu", 1928)

"They are so confident that they will run on forever. But they won’t run on. They don’t know that this is all one huge big blazing meteor that makes a pretty fire in space, but that some day it’ll have to hit." (Ray Bradbury, "Fahrenheit 451", 1953)

"Earth - it was a place where you could stop being afraid, a place where fear of suffocation was not, where fear of blowout was not, where nobody went berserk with the chokers or dreamed of poisoned air or worried about shorthorn cancer or burn blindness or meteorite dust or low-gravity muscular atrophy. A place where there was wind to blow your sweat away." (Walter M Miller Jr, "The Lineman", 1957)

"There were still some meteorites coming in, making bright little winks of fire where they bit into the plain. Deadly stingers out of nowhere, heading nowhere, impartially orbiting, random as rain, random as death. The debris of creation." (Walter M Miller Jr, "The Lineman", 1957)

"Whole worlds formed in a pregnant void: not spherical worlds merely, but dodeka-spherical, and those much more intricate than that. Not merely seven colors to play with, but seven to the seventh and to the seventh again. Stars vivid in the bright light. You who have seen stars only in darkness. be silent! Asteroids that they ate like peanuts, for now they were all metamorphic giants. Galaxies like herds of rampaging elephants. Bridges so long that both ends of them receded over the light-speed edges. Waterfalls, of a finer water, that bounced off galaxy clusters as if they were boulders." (Raphael A Lafferty, "Sky", 1971)

"Somewhere a star was going nova, a black hole was vacuuming space, a comet was combing its hair." (Kate Wilhelm, "Mrs. Bagley Goes to Mars", 1978)

"Storms of Cataclysm lashed the Cretaceous earth, vast fires raged, and cometary grit sifted through the roiling atmosphere, to blight and kill the wilting foliage, till the mighty Dinosauria, adapted to a world now shattered, fell in massed extinction, and the leaping machineries of Evolution were loosed in chaos, to re-populate the stricken Earth with strange new orders of being." (William Gibson & Bruce Sterling, "The Difference Engine", 1991)

"Terraforming is an ancient profession. Making your world more habitable began on the Earth itself, with thefirst dancing fire that warmed its builder’s cave; and everything since - everygreen world and asteroid and comet - is an enlargement on that first cozy cave." (Robert Reed, "A Place with Shade", 1995)

On Literature: On Galaxies (From Fiction to Science-Fiction)

"Presently nothing was left in the whole cosmos but darkness and the dark whiffs of dust that once were galaxies." (Olaf Stapledon, "Last and First Men: A Story of the Near and Far Future", 1930)

"This is the goal of all living, that the cosmos may be known, and admired, and that it may be crowned with further beauties. Nowhere and at no time, so far as we can tell, at least within our own galaxy, has the adventure reached further than in ourselves. And in us, what has been achieved is but a minute beginning. But it is a real beginning." (Olaf Stapledon, "Last and First Men: A Story of the Near and Far Future", 1930)

"All this long human story, most passionate and tragic in the living, was but an unimportant, a seemingly barren and negligible effort, lasting only for a few moments in the life of the galaxy. When it was over, the host of the planetary systems still lived on, with here and there a casualty, and here and there among the stars a new planetary birth, and here and there a fresh disaster." (Olaf Stapledon, "Star Maker", 1937)

"Escape, God how we all need escape from this tiny here. The need for it has motivated just about everything man has ever done in any direction other than that of the satisfaction of his physical appetites; it has led him along weird and wonderful pathways; it has led him into art and religion, ascetism Exploration and Adventure [sic] and astrology, dancing and drinking, poetry and insanity. All of these have been escapes because he has known only recently the true direction of escape - outward, into infinity and eternity, away from this little flat if rounded surface we’re born on and die on. This mote in the solar system, this atom in the galaxy." (Fredric Brown, "The Lights in the Sky Are Stars", 1953)

"There was so much knowledge in the galaxy and he knew so little of it, understood so little of the little that he knew. [...] Out among the stars lay a massive body of knowledge, some of it an extension of what mankind knew, some of it concerning matters which Man had not yet suspected, and used in ways and for purposes that Man had not as yet imagined. And never might imagine, if left on his own." (Clifford D Simak, "Way Station", 1963)

"For years astrophysicists have been racking their brains over the reason for the great difference in the amounts of cosmic dust in various galaxies. The answer, I think, is quite simple: the higher a civilization is, the more dust and refuse it produces. This is a problem more for janitors than for astrophysicists." (Stanislaw Lem, "Let Us Save the Universe (An Open Letter from Ijon Tichy, Space Traveller", 1966)

"When they [radio astronomers] grew weary at their electronic listening posts, when their eyes grew dim with looking at unrevealing dials and studying uneventful graphs, they could step outside their concrete cells and renew their dull spirits in communion with the giant mechanism they commanded, the silent, sensing instrument in which the smallest packets of energy, the smallest waves of matter, were detected in their headlong, eternal flight across the universe. It was the stethoscope with which they took the pulse of the all and noted the birth and death of stars, the probe with which, here on an insignificant planet of an undistinguished star on the edge of its galaxy, they explored the infinite." (James Gunn, "The Listeners", 1968)

"A single human brain can perceive pattern on the scale of stars and galaxie [...] and interpret it as Love.’" (Ursula K Le Guin, "Vaster Than Empires and More Slow", 1971)

"Whole worlds formed in a pregnant void: not spherical worlds merely, but dodeka-spherical, and those much more intricate than that. Not merely seven colors to play with, but seven to the seventh and to the seventh again. Stars vivid in the bright light. You who have seen stars only in darkness. be silent! Asteroids that they ate like peanuts, for now they were all metamorphic giants. Galaxies like herds of rampaging elephants. Bridges so long that both ends of them receded over the light-speed edges. Waterfalls, of a finer water, that bounced off galaxy clusters as if they were boulders." (Raphael A Lafferty, "Sky", 1971)