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The geospatial characteristics of transportation networks structurally constrain their features, and as a result, analysis methods designed for social networks typically fail to capture useful characteristics or make informative comparisons. In the case of road networks, natural constraints on the edge distribution weaken the ability of standard community detection algorithms to find clusters of nodes that align with natural neighborhood extents. We show that by adding edge weights based on the similarity of localized subgraph features, we can apply modularity-based community detection algorithms to uncover improved neighborhood shapes and extents. The use of local network characteristics allows the feature analysis to be completed in linear time, thus making the approach expandable to very large networks. We demonstrate this technique with an application to central Tokyo.

Triadic relationships are accepted to play a key role in the dynamics of social and political networks. Building on insights gleaned from balance theory in social network studies and from Boltzmann-Gibbs statistical physics, we propose a model to quantitatively capture the dynamics of the four types of triadic relationships in a network. Central to our model are the triads' incidence rates and the idea that those can be modeled by assigning a specific triadic energy to each type of triadic relation. We emphasize the role of the degeneracy of the different triads and how it impacts the degree of frustration in the political network. In order to account for a persistent form of disorder in the formation of the triadic relationships, we introduce the systemic variable temperature. In order to learn about the dynamics and motives, we propose a generic Hamiltonian with three terms to model the triadic energies. One term is connected with a three-body interaction that captures balance theory. The other terms take into account the impact of heterogeneity and of negative edges in the triads. The validity of our model is tested on four datasets including the time series of triadic relationships for the standings between two classes of alliances in a massively multiplayer online game (MMOG). We also analyze real-world data for the relationships between the \"agents\" involved in the Syrian civil war, and in the relations between countries during the Cold War era. We find emerging properties in the triadic relationships in a political network, for example reflecting itself in a persistent hierarchy between the four triadic energies, and in the consistency of the extracted parameters from comparing the model Hamiltonian to the data.

Polarization is a topic of intense interest among social scientists, but there is significant disagreement regarding the character of the phenomenon and little understanding of underlying mechanics. A first problem, we argue, is that polarization appears in the literature as not one concept but many. In the first part of the article, we distinguish nine phenomena that may be considered polarization, with suggestions of appropriate measures for each. In the second part of the article, we apply this analysis to evaluate the types of polarization generated by the three major families of computational models proposing specific mechanisms of opinion polarization.

According to hedonic pricing theory (HPT) market forces operate on individual characteristics of a good, and the price of a product is the aggregate of the price across those characteristics. The relationship between price and characteristics remains poorly understood because characteristic qualities are hard to quantify, people have varying levels of information about characteristics, and people have heterogeneous preferences over characteristics. By analyzing data from a large, market-driven virtual world we are able to test HPT, while largely avoiding these pitfalls. We find that a linear model with functional characteristics predicts the prices poorly, but a log-linear model performs quite well. Adding social characteristics to this log-linear model improves the predictions substantially. This work strongly supports HPT and demonstrates a \"rational\" calculus including social value.

The Hong and Page 'diversity trumps ability' result has been used to argue for the more general claim that a diverse set of agents is epistemically superior to a comparable group of experts. Here we extend Hong and Page's model to landscapes of different degrees of randomness and demonstrate the sensitivity of the 'diversity trumps ability' result. This analysis offers a more nuanced picture of how diversity, ability, and expertise may relate. Although models of this sort can indeed be suggestive for diversity policies, we advise against interpreting such results overly broadly.

In order to better understand the role of transportation convenience in location preferences, as well as to uncover transportation system patterns that span multiple modes of transportation, we analyze 500 locations in the Tokyo area using properties of their multimodal transportation networks. Multiple sets of measures are used to cluster regions by their transportation features and to classify them by their synergistic properties and dominant mode of transportation. We use twelve measures collected at five different radii for five distinct combinations of transportation networks to rank locations by their transportation characteristics. We introduce an additional 114 scores derived from the 300 measures to assess, among other things, access to public transportation, the effectiveness of each mode of transportation, and synergies among the modes of transportation. Additionally, we leverage those scores to classify our locations as being train-centric, bus-centric, or car-centric and to uncover geographic patterns in these characteristics. We find that business hubs, despite having low populations, are so conveniently reachable via train and road systems that they consistently achieve the highest sociability and convenience scores. Suburban regions have more serviceable bus systems, but lower connectivity overall resulting in lower reachable populations despite greater local populations. Even though Tokyo has the largest and densest public transportation system in the world we find that the road network consistently dominates the train and bus networks for all accessibility measures.

We model the flow of human capital and resources across multimodal transportation networks throughout the Greater Tokyo Area. Our transportation networks include trains, buses, and roads integrated with a walking network among a geographically grounded hexagonal grid and connecting nodes of different modes. The hexagonal grid holds data on both the working population and number of jobs from which we built probability distributions for the origins and destinations of commuting trips. Using both the network simplex method and stochastically generated origin-destination trips we estimate the population flows necessary to satisfy this demand. Rather than micro-simulations of actual commuting patterns, congestion, or route planning, our approach aims to uncover patterns in the aggregate flow of human resources to and from economic opportunities. We describe the details of the socioeconomic data, network generation, and the results of our exploratory analysis, then discuss the implications of these findings for transportation usage and future work.

Identifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness and k -shell, depending on the structure of the connectivity. We consider SIR and SIS propagation dynamics on a temporally-extruded network of observed interactions and measure the conditional marginal spread as the change in the magnitude of the infection given the removal of each agent at each time: its temporal knockout (TKO) score. We argue that this TKO score is an effective benchmark measure for evaluating the accuracy of other, often more practical, measures of influence. We find that none of the network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the search for effective predictive measures.

We apply variations and extensions of structural balance theory to analyze the dynamics of geopolitical relations using data from the virtual world Eve Online. The highly detailed data enable us to study the interplay of alliance size, power, and geographic proximity on the prevalence and conditional behavior of triads built from empirical political alliances. Through our analysis, we reveal the degree to which the behaviors of players conform to the predictions of structural balance theory and whether our augmentations of the theory improve these predictions. In addition to studying the time series of the proportions of triad types, we investigate the conditional changes in triad types and the formation of polarized political coalitions. We find that player behavior largely conforms to the predictions of a multipolar version of structural balance theory that separates strong and weak configurations of balanced and frustrated triads. The high degree of explanatory power of structural balance theory in this context provides strong support for both the theory and the use of virtual worlds in social science research.

Public discussions of political and social issues are often characterized by deep and persistent polarization. In social psychology, it's standard to treat belief polarization as the product of epistemic irrationality. In contrast, we argue that the persistent disagreement that grounds political and social polarization can be produced by epistemically rational agents, when those agents have limited cognitive resources. Using an agent-based model of group deliberation, we show that groups of deliberating agents using coherence-based strategies for managing their limited resources tend to polarize into different subgroups. We argue that using that strategy is epistemically rational for limited agents. So even though group polarization looks like it must be the product of human irrationality, polarization can be the result of fully rational deliberation with natural human limitations.