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In this paper we investigate two real crime-related networks, which are both bipartite. The bipartite networks are: a spatial network where crimes of various types are committed in different local government areas; and a dark terrorist network where individuals attend events or have common affiliations. In each case we analyse the communities found by a random-walk based algorithm in the primary weighted...
Methods of social network analysis are evolving into the practice of analyzing multi-layer networks, which represent different dimensions of activity and relationships. This also applies to methods oriented to detect communities in order to analyze the relationship between groups on different layers. In this context, the purpose of this article is to answer the question of how much the communities...
A social role is a special position an individual possesses within a network, which indicates his or her behaviours, expectations, and responsibilities. Identifying the roles that individuals play in a social network has various direct applications, such as detecting influential members, trustworthy people, idea innovators, etc. Roles can also be used for further analyses of the network, e.g. community...
Modularity is a widely used measure for evaluating community structure in networks. The definition of modularity involves a comparison between the observed network and a null model, which serves as a reference. To make the comparison significant, this null model should characterize some features of the observed network. However, the previously used null models are not good representations of real-world...
Users in social networks use hashtags for various reasons, some of them being serving search purposes, gaining attention or popularity or starting new conversation - thus, creating viral memes. In this paper we address the problem of classifying these hashtags in different categories, based on whether they represent a real life event or a social network generated meme. We compute a set of language-agnostic...
In this paper we outline important differences between (1) protein interaction networks and (2) social and other complex networks, in terms of fine-grained network community profiles. While these families of networks present some general similarities, they also have some stark differences in the way the communities are formed. Namely, we find that the sizes of the best communities in such biological...
Social networking services are attracting increasing interest in the domain of community discovery. In social networks, the interactions among users are very frequent by sending emails, posting tweets, and sharing comments online, etc. Such networks usually include rich sentiment information, which can provide us with useful resources for identifying communities with different sentiment-topic distributions...
Community structure as a significant feature helps us understand networks in a mesoscopic view. Existing approaches for community detection haven't considered about the formation of communities, whereas community in real social networks is usually established around influential nodes. In this paper, we present an efficient and effective framework based on local influence to detect both overlapping...
Many methods have been proposed to detect communities in complex networks, but very little work has been done regarding their interpretation. In this work, we propose an efficient method to tackle this problem. We first define a sequence-based representation of networks, combining temporal information, topological measures and nodal attributes. We then describe how to identify the most emerging sequential...
Far beyond relationship topology, today's online social networks are also characterized by semantically rich text messages exchanged among users as well as GPS locations associated with those messages, as evidenced by Twitter's geotagged tweets. Textual contents help characterize users' personal interests, while geographical features help link users' behaviors in the online world to those in the physical...
In the context of Twitter, social capitalists are users trying to increase their number of followers and interactions by any means. They are not healthy for the service, because they introduce a bias in the way user influence and visibility are perceived. Understanding their behavior and position in the network is thus of important interest. In this work, we propose to do so by focusing on the community...
Mining the silent members, also called lurkers, of an online community has been recognized as an important problem that accompanies the extensive use of social networks. Existing solutions to the ranking of lurkers can aid understanding the lurking behaviors in social networks, however they ignore any information concerning the time dimension. In this work we push forward research in lurker mining...
Most of overlapping community detection algorithms cannot be applied to networks with highly overlapping community such as online social networks where individuals belong to many communities. One important reason is that many algorithms detect communities based on the explicit borders where nodes have more connections inside the communities, however, when the vertices' membership number gets large,...
We propose a generative probabilistic approach to modeling interactions in directed graphs for unveiling the participation of nodes in multiple communities along with the roles played therein. Precisely, a hierarchical Bayesian model is developed, in which node interactions are governed by latent explicative reasons regarded as personal and contextual interaction factors. The former are inherently...
Community detection is one of the most important problems in social network analysis in the context of the structure of the underlying graphs. Many researchers have proposed their own methods for discovering dense regions in social networks. Such methods are only designed with links of the underlying social network. However, with the development of recent applications, rich edge content can be available...
We present our novel community mining algorithm that uses only local information to accurately identify communities, outliers, and hubs in social networks. The main component of our algorithm is the T metric, which evaluates the relative quality of a community by considering the number of internal and external triads (3-node cliques) it contains. Furthermore we propose an intuitive statistical method...
We consider the problem of determining the structural differences between different types of social networks and using these differences for applications concerning prediction of their structures. Much research on this problem has been conducted in the context of social media such as Facebook and Twitter, within which one would like to characterize and classify different types of individuals such...
Most real-world social networks are inherently dynamic and composed of communities that are constantly changing in membership. As a result, recent years have witnessed increased attention toward the challenging problem of detecting evolving communities. This paper presents a game-theoretic approach for community detection in dynamic social networks in which each node is treated as a rational agent...
Community detection in inhomogeneous structured network is an attractive research problem that searches for methods to discover groups in which individuals are more densely interconnected with each other with higher probability of internal information propagation. While most of the previous approaches attempt to divide networks into communities according to the algorithm results of network or edge...
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