What Can the Temporal Social Behavior Tell Us? An Estimation of Vertex-Betweenness Using Dynamic Social Information

Abstract—The vertex-betweenness centrality index is an es-sential measurement for analyzing social networks, but the computation time is excessive. At present, the fastest algorithm, proposed by Brandes in 2001, requires O(|V ||E|) time, which is computationally intractable for real-world social networks that usually contain millions of nodes and edges. In this paper, we propose a fast and accurate algorithm for estimating vertex-betweenness centrality values for social networks. It only requires O(b2|V |) time, where b is the average degree in the network. Significantly, we demonstrate that the local dynamic information about the vertices is highly relevant to the global betweenness values. The experiment results show that the vertex-betweenness values estimated by the proposed model are close to the real values and their rank is fairly accurate. Furthermore, using data from online role-playing games, we present a new type of dynamic social network constructed from in-game chatting activity. Besides using such online game networks to evaluate our betweenness estimation model, we report several interesting findings derived from conducting static and dynamic social network analysis on game networks. Index Terms—Betweenness, MMORPG, Text-Conversation I

Characterization of Large Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction

Several experiments evidence that specialized brain regions functionally interact and reveal that the brain processes and integrates information in a specific and structured manner. Networks can be used to model brain functional activities constituting a way to characterize and quantify this structured form of organization. Reports state that different physiological states or even diseases that affect the central nervous system may be associated to alterations on those networks, that might reflect in graphs of different architectures. However, the relation of their structure to different states or conditions of the organism is not well comprehended. Thus, experiments that involve the estimation of functional neural networks of subjects exposed to different controlled conditions are of great relevance. Within this context, this research has sought to model large scale functional brain networks during an anesthetic induction process. The experiment was based on intra-cranial recordings of neural activities of an old world macaque of the species Macaca fuscata. Neural activity was recorded during a Ketamine-Medetomidine anesthetic induction process. Networks were serially estimated in time intervals of five seconds. Changes were observed in various networks properties within about one and a half minutes after the administration of the anesthetics. These changes reveal the occurrence of a transition on the networks architecture. During general anesthesia a reduction in the functional connectivity and network integration capabilities were verified in both local and global levels. It was also observed that the brain shifted to a highly specific and dynamic state. The results bring empirical evidence and report the relation of the induced state of anesthesia to properties of functional networks, thus, they contribute for the elucidation of some new aspects of neural correlates of consciousness.Comment: 28 pages , 9 figures, 7 tables; - English errors were corrected; Figures 1,3,4,5,6,8 and 9 were replaced by (exact the same)figures of higher resolution; Three(3) references were added on the introduction sectio

Prediction Approach of Critical Node Based on Multiple Attribute Decision Making for Opportunistic Sensor Networks

Predicting critical nodes of Opportunistic Sensor Network (OSN) can help us not only to improve network performance but also to decrease the cost in network maintenance. However, existing ways of predicting critical nodes in static network are not suitable for OSN. In this paper, the conceptions of critical nodes, region contribution, and cut-vertex in multiregion OSN are defined. We propose an approach to predict critical node for OSN, which is based on multiple attribute decision making (MADM). It takes RC to present the dependence of regions on Ferry nodes. TOPSIS algorithm is employed to find out Ferry node with maximum comprehensive contribution, which is a critical node. The experimental results show that, in different scenarios, this approach can predict the critical nodes of OSN better

Graph manipulations for fast centrality computation

The betweenness and closeness metrics have always been intriguing and used in many analyses. Yet, they are expensive to compute. For that reason, making the betweenness and closeness centrality computations faster is an important and well-studied problem. In this work, we propose the framework, BADIOS, which manipulates the graph by compressing it and splitting into pieces so that the centrality computation can be handled independently for each piece. Although BADIOS is designed and fine-tuned for exact betweenness and closeness centrality, it can easily be adapted for approximate solutions as well. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types and sizes of networks. In particular, it reduces the betweenness centrality computation time of a 4.6 million edges graph from more than 5 days to less than 16 hours. For the same graph, we achieve to decrease the closeness computation time from more than 3 days to 6 hours (12.7x speedup)

Networks in cognitive science

Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions, and collaborations among scientists. Today, the inclusion of network theory into Cognitive Sciences, and the expansion of complex-systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the Cognitive Sciences.Postprint (author's final draft

Different roles of nodes in networks

The 'complex network' has been studied in many disciplines. In this thesis, we use an economic network to study the heterogeneity of the networks. Networks of shareholders in Turkey and the Netherlands are constructed from raw data. The nodes are shareholders and an edge between shareholders exists if they have invested in the same company. The general analysis of network has shown that this type of network has characteristics similar to other types of real-world networks: power-law like degree distributions, small-world phenomenon and scaling of community size distributions. Furthermore, we introduce the 'type' of shareholders and analyse the different behaviour of shareholder types by comparing with a randomised null model. The results are that different types of shareholders are parts of different topological structures in the networks. Based on the economic behaviours, we propose a random walk model to mimic the different roles of shareholders in the networks. The model starts with a directed random graph of shareholders with assigned labels/types mimicing the raw data, and companies, showing which companies shareholders have invested in. A biased random walker model is introduced to model, on an abstract level, how shareholders' investments evolve. We then extract the associated shareholder network. This evolving model can qualitatively explain general characteristics and heterogeneity of the real-world shareholder networks: the scaling of community size distributions, percolation behaviour and the average shortest paths between different types. When we focus on the emergence of features from local interactions and higher-order interactions. We propose a new framework for this analysis. For a more general analysis, we design a simple transition matrix of temporal triplets. By comparing the transition matrix of higher-order interactions with the transition matrix of a pairwise interaction toy model, we can quantify the interactions of triplets. Moreover, we create an algorithm based on the transition matrix to make link predictions. We apply this framework to real-world networks and show that this new framework is successful in making predictions.Open Acces

Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease

Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.This research has been financed by Carlos III Health Institute (ISCIII) under the project COV20-00881 and the project Fundación BBVA.DGVI 256/22 “COVID-19 Urban Atlas Spain”.S

Protection against Contagion in Complex Networks

In real-world complex networks, harmful spreads, commonly known as contagions, are common and can potentially lead to catastrophic events if uncontrolled. Some examples include pandemics, network attacks on crucial infrastructure systems, and the propagation of misinformation or radical ideas. Thus, it is critical to study the protective measures that inhibit or eliminate contagion in these networks. This is known as the network protection problem. The network protection problem investigates the most efficient graph manipulations (e.g., node and/or edge removal or addition) to protect a certain set of nodes known as critical nodes. There are two types of critical nodes: (1) predefined, based on their importance to the functionality of the network; (2) unknown, whose importance depends on their location in the network structure. For both of these groups and with no assumption on the contagion dynamics, I address three major shortcomings in the current network protection research: namely, scalability, imprecise evaluation metric, and assumption on global graph knowledge. First, to address the scalability issue, I show that local community information affects contagion paths through characteristic path length. The relationship between the two suggests that, instead of global network manipulations, we can disrupt the contagion paths by manipulating the local community of critical nodes. Next, I study network protection of predefined critical nodes against targeted contagion attacks with access to partial network information only. I propose the CoVerD protection algorithm that is fast and successfully increases the attacker’s effort for reaching the target nodes by 3 to 10 times compared to the next best-performing benchmark. Finally, I study the more sophisticated problem of protecting unknown critical nodes in the context of biological contagions, with partial and no knowledge of network structure. In the presence of partial network information, I show that strategies based on immediate neighborhood information give the best trade-off between performance and cost. In the presence of no network information, I propose a dynamic algorithm, ComMit, that works within a limited budget and enforces bursts of short-term restriction on small communities instead of long-term isolation of unaffected individuals. In comparison to baselines, ComMit reduces the peak of infection by 73% and shortens the duration of infection by 90%, even for persistent spreads