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Social network is becoming indispensable of people's lives in recent years. Community detection on real network continues to be a hotspot in data ming domain. As users may join multiple social circles and interest communities, and an abundance of information can be a reflection of users' preference, heterogeneous information fusion and overlapping community detection are two key issues researchers...
In real life networks like social and biological networks, the network is said to have community structure if the vertices of the network can be partitioned into groups of nodes such that each group contains the nodes that are densely connected in the original graph that represents this network. These groups are referred to as communities. Detecting and identifying communities in networks is essential...
Nowadays, social network sites, such as Facebook and Twitter, have tremendous number of users in their repositories. Having this huge amount of data requires analyzing them to get statistics about the users and their interests. In this paper, we propose a new algorithm that clusters the nodes in social networks into communities based on their geodesic location and the similarity between their interests...
Community detection is an important field in research of social networks. There exist a lot of algorithms which most of them are based on the density of connections between groups of nodes. On the one hand, the error and lack of links may lead to great impact on the result of community detection. On the other hand, there are users with deep relation but without much communication, so the density of...
A persona in a social network is defined as the person's activities and attributes in a social network as seen by others. And a community in a social network is defined as a group of users in that social network which share common interests and are most likely to interact with each other in the network. For community detection, a user's persona and its connections with the other users in a network,...
Many phenomena in our world can be modeled as networks, from neurons in the human brain, computer networks and bank transactions to social interactions. Anomaly detection is an important data mining task consisting in detecting rare objects that deviate from the majority of the data. Contextual collective anomaly detection techniques can be applied to intrusion detection in computer networks, bank...
Community detection is a hot topic for researchers in the fields including graph theory, social networks and biological networks. Generally speaking, a community refers to a group of densely linked nodes in the network. Nodes usually have more than one community label, indicating their multiple roles or functions in the network. Unfortunately, existing solutions aiming at overlapping-community-detection...
Social networks have gained a lot of interest in recent literature due to the huge amount of data that can be extracted from them. With this ever growing data, emerged the need for techniques to handle it and analyze it. Several papers have proposed many techniques to analyze a given social network from several aspects. Communities are a crucial property in social networks and community detection...
Recently, opinion leader discovery has drawn much attention due to its widespread applicability. By identifying the opinion leader, companies or governments can manipulate the selling or guiding public opinion, respectively. However, mining opinion leader is a challenge task because of the complexity of processing social graph and analyzing leadership quality. In this study, a novel method, TCOL-Miner,...
Nowadays, the emergence of online social networks have empowered people to easily share information and media with friends. Interacting users of social networks with similar users and their friends form community structures of networks. Uncovering communities of the online users in social networks plays an important role in network analysis with many applications such as finding a set of expert users,...
In recent years, community detection in overlapping weighted network became a research challenge. In real networks, a node can belong to two or more communities. Therefore, in this paper, we aim to address the above-mentioned problem by proposing a method to improve the modularity in overlapping weighted networks. The proposed method is based on optimizing a fitness function and fuzzy belonging degree...
Determining the frequencies and the distribution of small subgraph patterns in a large input graph is an important part of many graph based mining tasks such as Frequent Subgraph Mining (FSM) and Motif Detection. Due to the exponential number of such graph patterns the interpretation of the mining results is mostly limited to finding unexpectedly frequent patterns, and in general identifying few particularly...
Community detection has become one of the most important methods for studying social networks. However, most of the existing community detection algorithms may not be applicable to mobile social networks due to their complexity. To solve this problem, we present a parallel algorithm to conduct community detection based on general stochastic block (GSB) model. We first model a mobile social network...
In this paper we present the first case study on collaboration network of researchers at the University of Montenegro - UoM. We identify the largest clusters or groups of scientists that are interested in the same topic, using Girvan-Newman algorithm. The results show that these clusters constantly grow over the period 2005–2015 and at the moment they occupy more than 50% authors from the UoM. It...
The seminal works by Karger [13], [14] have shown that one can use Uniform Random Edge (URE) sampling to generate a graph skeleton which accurately approximates all cut-values in the original graph with high probability under some specific assumptions. As such, the random subgraphs resulted from URE sampling can often be used as substitutes for the original graphs in cut/flow-related graph-optimization...
Network communities exist as clusters of nodes whose intra-edge connectivity is stronger than edge connectivities between nodes from different clusters. Among others, identification of hidden communities unveils shared functional roles in biological networks, and assigns individuals in social networks to consumer groups for more targeted advertising. This is a rather challenging task in large-size...
Heterogeneous networks have become an important model to represent complex network. However, many existing community detection methods for dynamic network are hardly applied in heterogeneous networks. In this paper, we present a multi-view learning based algorithm for dynamic heterogeneous networks, which treats network individually, combines heterogeneous information and improves the quality. Compared...
In this paper we propose two algorithms for overlapping community detection based on neighborhood vector propagation algorithm(NVPA), a community detection algorithm which can detect disjoint communities with high accuracy. The first algorithm is named Link Partition of Overlapping Communities (LPOC). In this algorithm, we first convert a node graph to a link graph, then we use NVPA to find the communities...
Social network community detection has occupied an important place in many scientific fields like biology, sociology, or computer science. This problem still attracted a lot of work. The challenge is how to identify inside these networks, groups of persons strongly linked and sharing the same preferences. As in the literature, there are many works trying to detect communities we tried in this paper...
Social network analysis has attracted extensive research attention recently. By setting persons or more general entities as network vertices and using edges to represent their interactions, network provides an effective tool in representing complex social relations. Dividing such a network into different clusters in accordance with its inherent structure has been widely investigated. A method called...
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