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Social network analysis is an important set of techniques that are used in many different areas. One such area is intelligence and law enforcement where social network analysis is used to study various kinds of networks. One of the problems with social networks that are extracted from social media is that easily becomes very large and as a consequence difficult to analyze. Therefore, there is a need...
The problem of node classification has been widely studied in a variety of network-based scenarios. In this paper, we will study the more challenging scenario in which some of the edges in a content-based network are labeled, and it is desirable to use this information in order to determine the labels of other arbitrary edges. Furthermore, each edge is associated with text content, which may correspond...
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...
We propose estimators for popular clustering coefficient measures 1) network average clustering coefficient and 2) global clustering coefficient (aka transitivity). Unlike most of previous studies estimating clustering coefficients, we do not use independent vertex sampling as it is either unavailable or inefficient to implement in most Online Social Networks (OSNs). Instead, we propose estimators...
Online social network services now have generally enormous monthly active users. Each user may have hundreds of different ties to families, friends or acquaintances. Discovering multiple social ties is pivotal in understanding the human relationship and recognizing the role played by individuals in very large networks. In this paper, an incremental Dirichlet process Gaussian mixture model is introduced...
In this paper, we propose a new algorithm, called STRICLUSTER, to find tri-clusters from signed 3-partite graphs. The dataset contains three different types of nodes. Hyperedges connecting three nodes from three different partitions represent either positive or negative relations among those nodes. The aim of our algorithm is to find clusters with strong positive relations among its nodes. Moreover,...
Protein complexes are key entities in the cell responsible for various cellular mechanisms and biological processes. We propose here a method for predicting protein complexes from a protein-protein interaction (PPI) network, using information on mutually exclusive PPIs. If two interactions are mutually exclusive, they are not allowed to exist simultaneously in the same predicted complex. We introduce...
One highly studied aspect of social networks is the identification of influential nodes that can spread ideas in a highly efficient way. The vast majority of works in this field have investigated the problem of identifying a set of nodes, that if "seeded" simultaneously, would maximize the information spread in the network. Yet, the timing aspect, namely, finding not only which nodes should...
Identifying communities has always been a fundamental task in analysis of complex networks. Many methods have been devised over the last decade for detection of communities. Amongst them, the label propagation algorithm brings great scalability together with high accuracy. However, it has one major flaw; when the community structure in the network is not clear enough, it will assign every node the...
In community detection, the theme of correctly identifying overlapping nodes, i.e. nodes which belong to more than one community, is important as it is related to role detection and to the improvement of the quality of clustering: proper detection of overlapping nodes gives a better understanding of the community structure. In this paper, we introduce a novel measure, called cuttability, that we show...
Large-scale temporal graphs can serve as a model in many application scenarios. Recently, due to the popularity of online social networks and increased research interest in reality mining i.e. gathering and analyzing data about human behavior and interaction in the real world, temporal graphs gain traction in social network analysis and more specifically in the analysis of dynamic processes in social...
Overlapping community detecting for large-scale social networks becomes a research focus with the development of online social network applications. Among the current overlapping community discovery algorithms, LFM is based on local optimization of a fitness function, which is in consistent with the local nature of community, especially in large networks. But the original LFM may fall in loops when...
Motivated by the observation that communities in real world social networks form due to actions of rational individuals in networks, we propose a novel game theory inspired algorithm to determine communities in networks. The algorithm is decentralized and only uses local information at each node. We show the efficacy of the proposed algorithm through extensive experimentation on several real world...
This paper proposes an unsupervised method for automatic identification of spammers in a social network. In our approach, we first investigate the link structure of the network in order to derive a legitimacy score for each node. Then we model these scores as a mixture of beta distributions. The number of components in the mixture is determined by the integrated classification likelihood Bayesian...
When analyzing social networks, centrality and community identification are among the most popular topics for researchers. Depending on their motivation and the resulting hypothesis, they usually focus on one of these two structural properties, leaving the other aspect aside. In this paper we investigate the relation between structural centralization, which follows a core/periphery model, and structural...
Studies of community structure and evolution in large social networks require a fast and accurate algorithm for community detection. As the size of analyzed communities grows, complexity of the community detection algorithm needs to be kept close to linear. The Label Propagation Algorithm (LPA) has the benefits of nearly-linear running time and easy implementation, thus it forms a good basis for efficient...
This paper describes an automatic text analysis of values contained in the Enron email dataset that seeks to explore the potential to apply value patterns to cluster a social network. Two hypotheses are posed: individuals communicate more frequently with other individuals who share similar value patterns than with individuals with different value patterns; and people who communicate more frequently...
Some methods for object group identification applicable for social group identification are compared. We suppose that people are characterized by their actions, for example the deputies are characterized by their voting habits. We are interested in binary data analysis (e.g. the result of voting is yes or not). The dataset consisting of the roll-call votes records in the Russian parliament in 2004...
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