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Register a new userDetecting global bridges in networks
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The identification of nodes occupying important positions in a network structure is crucial for the understanding of the associated real-world system. Usually, betweenness centrality is used to evaluate a node capacity to connect different graph regions. However, we argue here that this measure is not adapted for that task, as it gives equal weight to local centers (i.e. nodes of high degree central to a single region) and to global bridges, which connect different communities. This distinction is important as the roles of such nodes are different in terms of the local and global organisation of the network structure. In this paper we propose a decomposition of betweenness centrality into two terms, one highlighting the local contributions and the other the global ones. We call the latter bridgeness centrality and show that it is capable to specifically spot out global bridges. In addition, we introduce an effective algorithmic implementation of this measure and demonstrate its capability to identify global bridges in air transportation and scientific collaboration networks.
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Online social networks are often subject to influence campaigns by malicious actors through the use of automated accounts known as bots. We consider the problem of detecting bots in online social networks and assessing their impact on the opinions of individuals. We begin by analyzing the behavior of bots in social networks and identify that they exhibit heterophily, meaning they interact with humans more than other bots. We use this property to develop a detection algorithm based on the Ising model from statistical physics. The bots are identified by solving a minimum cut problem. We show that this Ising model algorithm can identify bots with higher accuracy while utilizing much less data than other state of the art methods. We then develop a a function we call generalized harmonic influence centrality to estimate the impact bots have on the opinions of users in social networks. This function is based on a generalized opinion dynamics model and captures how the activity level and network connectivity of the bots shift equilibrium opinions. To apply generalized harmonic influence centrality to real social networks, we develop a deep neural network to measure the opinions of users based on their social network posts. Using this neural network, we then calculate the generalized harmonic influence centrality of bots in multiple real social networks. For some networks we find that a limited number of bots can cause non-trivial shifts in the population opinions. In other networks, we find that the bots have little impact. Overall we find that generalized harmonic influence centrality is a useful operational tool to measure the impact of bots in social networks.
We consider social networks of competing agents that evolve dynamically over time. Such dynamic competition networks are directed, where a directed edge from nodes $u$ to $v$ corresponds a negative social interaction. We present a novel hypothesis that serves as a predictive tool to uncover alliances and leaders within dynamic competition networks. Our focus is in the present study is to validate it on competitive networks arising from social game shows such as Survivor and Big Brother.
In network science, assortativity refers to the tendency of links to exist between nodes with similar attributes. In social networks, for example, links tend to exist between individuals of similar age, nationality, location, race, income, educational level, religious belief, and language. Thus, various attributes jointly affect the network topology. An interesting problem is to detect community structure beyond some specific assortativity-related attributes $rho$, i.e., to take out the effect of $rho$ on network topology and reveal the hidden community structure which are due to other attributes. An approach to this problem is to redefine the null model of the modularity measure, so as to simulate the effect of $rho$ on network topology. However, a challenge is that we do not know to what extent the network topology is affected by $rho$ and by other attributes. In this paper, we propose Dist-Modularity which allows us to freely choose any suitable function to simulate the effect of $rho$. Such freedom can help us probe the effect of $rho$ and detect the hidden communities which are due to other attributes. We test the effectiveness of Dist-Modularity on synthetic benchmarks and two real-world networks.
Multilayer networks allow for modeling complex relationships, where individuals are embedded in multiple social networks at the same time. Given the ubiquity of such relationships, these networks have been increasingly gaining attention in the literature. This paper presents the first analysis of the robustness of centrality measures against strategic manipulation in multilayer networks. More specifically, we consider an evader who strategically chooses which connections to form in a multilayer network in order to obtain a low centrality-based ranking-thereby reducing the chance of being highlighted as a key figure in the network-while ensuring that she remains connected to a certain group of people. We prove that determining an optimal way to hide is NP-complete and hard to approximate for most centrality measures considered in our study. Moreover, we empirically evaluate a number of heuristics that the evader can use. Our results suggest that the centrality measures that are functions of the entire network topology are more robust to such a strategic evader than their counterparts which consider each layer separately.
Competition networks are formed via adversarial interactions between actors. The Dynamic Competition Hypothesis predicts that influential actors in competition networks should have a large number of common out-neighbors with many other nodes. We empirically study this idea as a centrality score and find the measure predictive of importance in several real-world networks including food webs, conflict networks, and voting data from Survivor.
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