Applications of Temporal Graph Metrics to Real-World Networks

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.Comment: 25 page

The neurocognitive gains of diagnostic reasoning training using simulated interactive veterinary cases

The present longitudinal study ascertained training-associated transformations in the neural underpinnings of diagnostic reasoning, using a simulation game named “Equine Virtual Farm” (EVF). Twenty participants underwent structural, EVF/task-based and resting-state MRI and diffusion tensor imaging (DTI) before and after completing their training on diagnosing simulated veterinary cases. Comparing playing veterinarian versus seeing a colorful image across training sessions revealed the transition of brain activity from scientific creativity regions pre-training (left middle frontal and temporal gyrus) to insight problem-solving regions post-training (right cerebellum, middle cingulate and medial superior gyrus and left postcentral gyrus). Further, applying linear mixed-effects modelling on graph centrality metrics revealed the central roles of the creative semantic (inferior frontal, middle frontal and angular gyrus and parahippocampus) and reward systems (orbital gyrus, nucleus accumbens and putamen) in driving pre-training diagnostic reasoning; whereas, regions implicated in inductive reasoning (superior temporal and medial postcentral gyrus and parahippocampus) were the main post-training hubs. Lastly, resting-state and DTI analysis revealed post-training effects within the occipitotemporal semantic processing region. Altogether, these results suggest that simulation-based training transforms diagnostic reasoning in novices from regions implicated in creative semantic processing to regions implicated in improvised rule-based problem-solving

A network model of interpersonal alignment in dialog

In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Keywords: alignment in communication; structural coupling; linguistic networks; graph distance measures; mutual information of graphs; quantitative network analysi

Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

Bots increase exposure to negative and inflammatory content in online social systems

Societies are complex systems which tend to polarize into sub-groups of individuals with dramatically opposite perspectives. This phenomenon is reflected -- and often amplified -- in online social networks where, however, humans are no more the only players, and co-exist alongside with social bots, i.e., software-controlled accounts. Analyzing large-scale social data collected during the Catalan referendum for independence on October 1, 2017, consisting of nearly 4 millions Twitter posts generated by almost 1 million users, we identify the two polarized groups of Independentists and Constitutionalists and quantify the structural and emotional roles played by social bots. We show that bots act from peripheral areas of the social system to target influential humans of both groups, bombarding Independentists with violent contents, increasing their exposure to negative and inflammatory narratives and exacerbating social conflict online. Our findings stress the importance of developing countermeasures to unmask these forms of automated social manipulation.Comment: 8 pages, 5 figure