This article investigates how parliamentary debate in the Dutch House of Representatives (“Tweede Kamer”) (1945–1995) narrowed as MPs turned into domain specialists. We call this narrowing epistemic capture: a few experts progressively bound what can be said. To detect epistemic capture, we deploy a three-layer computational pipeline. Latent-Dirichlet topic modeling converts 8.2 million sentences into 250 semantic themes; Pointwise Mutual Information networks connect themes within six-month windows; Louvain clustering traces the birth, drift and endurance of topical communities.

Capture appears on every scale. Macro-level: network modularity almost doubles after 1960 while density falls, marking compartmentalized debate. Meso-level: cabinet turnovers act as “reset switches”: topic-neighborhood similarity drops in the half-year after a new coalition forms, then anneals along partisan lines. Micro-level: enduring communities – foreign policy, agriculture and education – lock topics and MPs together for decades, yet resistance to capture is visible in distinct contentious topics.

These multiscale patterns show how 20th-century Dutch parliamentary debate saw a rise of technical specialism that significantly constrained the breadth of political debate. Methodologically, the study demonstrates the value of structural (network) distant reading over purely lexical counts and offers a transferable workflow for measuring how democratic discourse undergoes structural transformations.

Antisocial behavior can be contagious, spreading from individual to individual and rippling through social networks. Moreover, it can spread not only through third-party influence from observation, just like innovations or individual behavior do, but also through direct experience, via “pay-it-forward” retaliation. Here, we distinguish between the effects of observation and victimization for the contagion of antisocial behavior by analyzing large-scale digital trace data. We study the spread of cheating in more than a million matches of an online multiplayer first-person shooter game, in which up to 100 players compete individually or in teams against strangers. We identify event sequences in which a player who observes or is killed by a certain number of cheaters starts cheating and evaluate the extent to which these sequences would appear if we preserve the team and interaction structure but assume alternative gameplay scenarios. The results reveal that social contagion is only likely to exist for those who both observe and experience cheating, suggesting that third-party influence and “pay-it-forward” reciprocity interact positively. In addition, the effect is present only for those who both observe and experience more than once, suggesting that cheating is more likely to spread after repeated or multi-source exposure. Approaching online games as models of social systems, we use the findings to discuss strategies for targeted interventions to stem the spread of cheating and antisocial behavior more generally in online communities, schools, organizations, and sports.

Community-based physical activity programs, such as the Recreovía, are effective in promoting healthy behaviors in Latin America. To understand Recreovías’ challenges and scalability, we characterized its social network longitudinally while studying its participants’ social cohesion and interactions. First, we constructed the Main network of the program’s Facebook profile in 2013 to determine the main stakeholders and communities of participants. Second, we studied the Temporal network growth of the Facebook profiles of three Recreovía locations from 2008 to 2016. We implemented a Time Windows in Networks algorithm to determine observation periods and a scaling model of cities’ growth to measure social cohesion over time. Our results show physical activity instructors as the main stakeholders (20.84% nodes of the network). As emerging cohesion, we found: (1) incremental growth of Facebook users (43–272 nodes), friendships (55–2565 edges), clustering coefficient (0.19–0.21), and density (0.04–0.07); (2) no preferential attachment behavior; and (3) a social cohesion super-linear growth with 1.73 new friendships per joined user. Our results underscore the physical activity instructors’ influence and the emergent cohesion in innovation periods as a co-benefit of the program. This analysis associates the social and healthy behavior dimensions of a program occurring in natural environments under a systemic approach.

The link prediction task has found numerous applications in real-world scenarios. However, in most of the cases like interactions, purchases, mobility, etc., links can re-occur again and again across time. As a result, the data being generated is excessively large to handle, associated with the complexity and sparsity of networks. Therefore, we propose a very fast, memory-less, and dynamic sampling-based method for predicting recurring links for a successive future point in time. This method works by biasing the links exponentially based on their time of occurrence, frequency, and stability. To evaluate the efficiency of our method, we carried out rigorous experiments with massive real-world graph streams. Our empirical results show that the proposed method outperforms the state-of-the-art method for recurring links prediction. Additionally, we also empirically analyzed the evolution of links with the perspective of multi-graph topology and their recurrence probability over time.

In this paper, we attempt to investigate the attack tolerance of human mobility networks where the mobility is restricted to some extent, for instance, in a hospital, one is not allowed to access all locations. Similar situations also arise in schools. In such a network, we will show that people need to rely upon some intermediate agents, popularly known as the brokers to disseminate information. In order to establish this fact, we have followed the approach of attack in a network which in turn helps to identify important nodes in the network in order to maintain the overall connectivity. In this direction, we have proposed, a new temporal metric, brokerage frequency which significantly outperforms all other state-of-the-art attack strategies reported in Trajanovski et al. (2012), Sur et al. (2015).

Distributed information transfer is of paramount importance to the effectiveness of dynamic collective behaviors, especially when a swarm is confronted with complex environmental circumstances. Recently, the signaling network of interaction underlying such effective information transfers has been revealed in the particular case of bird flocks governed by a topological interaction. Such biological systems are known to be evolutionary optimized, but are also constrained by the very nature of the signaling mechanisms—owing to intrinsic limitations in sensory modalities—enabling communication among individuals. Here, we propose that artificial swarm design can be tackled from the angle of signaling network design. To this aim, we use different network models to investigate the impact of some network structural properties on the effectiveness of a specific emergent swarming behavior, namely global consensus. Two new network models are introduced, which together with the well-known Watts–Strogatz model form the basis for an analysis of the relationship between clustering, shortest path and speed to consensus. A network-theoretic approach combined with spectral graph theory tools are used to propose some signaling network design principles. Eventually, one key design principle—a concomitant reduction in clustering and connecting path—is successfully tested on simulations of swarms of self-propelled particles.

Empirical data on contacts between individuals in social contexts play an important role in providing information for models describing human behavior and how epidemics spread in populations. Here, we analyze data on face-to-face contacts collected in an office building. The statistical properties of contacts are similar to other social situations, but important differences are observed in the contact network structure. In particular, the contact network is strongly shaped by the organization of the offices in departments, which has consequences in the design of accurate agent-based models of epidemic spread. We consider the contact network as a potential substrate for infectious disease spread and show that its sparsity tends to prevent outbreaks of rapidly spreading epidemics. Moreover, we define three typical behaviors according to the fraction f of links each individual shares outside its own department: residents, wanderers, and linkers. Linkers (f ~ 50%) act as bridges in the network and have large betweenness centralities. Thus, a vaccination strategy targeting linkers efficiently prevents large outbreaks. As such a behavior may be spotted a priori in the offices' organization or from surveys, without the full knowledge of the time-resolved contact network, this result may help the design of efficient, low-cost vaccination or social-distancing strategies.