Analyzing Community Core Evolution in Mobile Social Networks

The topic of community detection in social networks has attracted a lot of attention in recent years. Existing methods always depict the relationship of two nodes using the snapshot of the network, but these snapshots cannot reveal the real relationships, especially when the connection history among nodes is considered. The problem of detecting the stable community in mobile social networks has been studied in this paper. Community cores are considered as stable subsets of the network in previous work. Based on these observations, this paper divides all nodes into a few of communities due to the community cores. Meanwhile, communities can be tracked through incremental computing. Experimental results based on real-world social networks demonstrate that our proposed method performs better than the well-known static community detection algorithm in mobile social networks.

Advances in Web 2.0 technologies have led to unstructured information overload in the complex and multidimensional datasets that originate in social networks. This overload can diminish the quality of web-related services such as recommender systems in the social web and can overwhelm users with irrelevant information. Such overload indicates the need to design and develop efficient and accurate information management and retrieval systems in social networks and anticipate recommendations in the semantic context of user interest. Community detection in social networks can help identify higher-order structures that unveil insight into networks and their functional organization and reduce irrelevant information received by the user. In this study, the application of community detection in online social networks is investigated within the framework of topic discovery-based user link identification and is consequently used to uncover implicit user communities (ad hoc communities). In the proposed approach, we use a graph-based information extraction technique that provides for a personalized information retrieval (recommender) system. We hypothesize that the ad hoc communities of users sharing similar interests embedded in a folksonomy-based social network can be identified by overlapping tag clusters in the tag concept hierarchy. Our methodology incorporates the novel information extraction techniques of topic modeling for topic extraction (feature extraction), user profile modeling for user profile extraction, and community extraction from the social graph, modeled in a framework to derive relevant ad hoc user communities of social interest from folksonomy data. Our experimental results demonstrate an accuracy of 70–98 % in community detection using data obtained from CiteULike®.

Community evolution in social networks attracts a lot of attention in recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. Cumulative stable contacts are proposed to depict the correlation among nodes. The whole process is divided into timestamps. At each timestamp, the community cores will be detected due to the variation of nodes and links firstly. Then, all nodes will be divided into a few of communities due to the community cores. Meanwhile, communities can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community in mobile social networks.

Community detection is an effective approach to unveil relationships among individuals in online social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties, such as advertisement companies and hospitals, with access to social networks for different purposes, which can easily lead to a privacy breach. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topology and the users' attributes. We show that with additional prior knowledge, community detection can be performed by querying the information of only a fraction of instead of the entire population. In particular, we first propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Then, we propose a parsimonious node affiliation recovery (NAR) algorithm, which is also differentially private, to unveil the community affiliation information of the whole population based on that of the limited number of queried individuals by solving a sparse optimization problem. Through both theoretical analysis and experimental validation using synthetic and real-world social networks, we demonstrate that the proposed DPCD scheme detects social communities under the modest privacy budget. In addition, we show the effectiveness of NAR to perform community detection by querying a limited number of individuals in social networks.

Parallel and distributed paradigms for community detection in social networks: A methodological review

With the rapid increase in popularity of online social networks, community detection in these networks has become a key aspect of research field. Overlapping community detection is an important NP-hard problem of social network analysis. Modularity-based community detection is one of the most widely used approaches for social network analysis. However, modularity-based community detection technique may fail to resolve small-size communities. Hence, we propose a novel two-step approach for overlapping community detection in social networks. In the first step, modularity density-based hybrid meta-heuristics approach is used to find the disjoint communities and the quality of these disjoint communities can be verified using Silhouette coefficient. In the second step, the quality disjoint communities with low computation cost are used to detect overlapping nodes based on Min-Max Ratio of minimum(indegree, outdegree) to the maximum(indegree, outdegree) values of nodes. We tested the proposed algorithm based on 10 standard community quality metrics along with Silhouette score using seven standard datasets. Experimental results demonstrate that the proposed approach outperforms the current state-of-the-art works in terms of quality and scalability.

The progress of website and different social network sites (SNS) have made the clients to communicate on a collective environment. A graph is applied to denote the working of social networks. Graph is nothing but a collection of nodes and edges, where the edges are used to connect the nodes. In social networks, the individuals / entities are represented by nodes and also the edges represent the interaction between entities. The disposition of individuals with connected perceptions, decisions and inclinations is connected with a social networking model, which ends up in the event of virtual clusters or communities. Revealing those groups is useful for frequent claims by discovering a typical analysis space network, discovering the fact of finding a set of similar minded individuals for promoting proposals and to discover macromolecule interface network in biological system. In the literature review, an outsized variety of detecting the community algorithm is proposed and applied to various domains. This research study presents a review of the prevailing algorithms and methodologies used for communities' detection in social networking. Also, this research work tends to conjointly deliberate a number of applications for Community Detection (CD) in social media networks.

Revealing the community structure in social networks witnessed a determined effort. In this respect, a different category of social network can be handled, such as, dynamic social networks, social networks with node attributes, etc. In this article, we introduce a new method to solve this thriving issue in the social network with node attributes. This latter can be represented by a bipartite graph, which consists of a two sets of nodes and edges connecting these nodes. The tendency of people with similar node attributes leads to the hidden information of clusters or communities. A wealthy number of community-detection algorithms have been proposed for bipartite graphs and applied to several domains in the literature. To palliate some of the highlighted shortcomings, we introduce a new approach, called Fast-Bi Community Detection (FBCD), that aims to an efficient community detection in social networks. The main idea of this approach is to explore the set of maximum matching in the bipartite graph in order to reduce the complexity of our algorithm. The carried out experiments show the high quality of the obtained communities versus those by the pioneering ones of the literature.

Network contents including node contents and edge contents can be utilized for community detection in social networks. Thus, the topic of each community can be extracted as its semantic information. A plethora of models integrating topic model and network topologies have been proposed. However, a key problem has not been resolved that is the semantic division of a community. Since the definition of community is based on topology, a community might involve several topics. To ach

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relative features of graphs representing in real systems is community detection. The community detection can be considered as fairly independent compartments of a graph and a similar role play. It is an important problem in the analysis of computer networks, social networks, biological networks and many other natural and artificial networks. Thus, these networks are in general very large. And the finding hidden structures and functional modules are very hard tasks. This problem is very hard and not yet satisfactorily solved. Many methods have been intended to deal with this problem in networks. Some of the most expectation are methods based on statistical inference, which support on solid mathematical foundations and return excellent results in practice. In this paper we show the blockmodeling, a collection of methods for partitioning networks according to well-specified criteria. We use the term "blockmodeling" to characterize the usual approach to blockmodeling, which based on the concepts of structural equivalence and regular equivalence. We also gives the idea about how community is detected in social networking by Euclidean distance algorithm and REGE algorithm.

Understanding the Design of Mobile Social Networking

Community detection in social networks is a promising research area. However, network exploration with multiple types of relationships remains an underexplored yet crucial domain. To address the challenge of community detection in multiplex networks, we propose SFCSA, a smart flattening-based Cuckoo Search Algorithm, designed to address the complexities of such networks effectively. SFCSA unfolds in three integral steps. Firstly, we employ DeepWalk and node2vec algorithms to extract node embeddings, which are merged to construct the multiplex network node embeddings, minimizing information loss. Secondly, a flattened network is built by merging the network layers and adopting the Pearson correlation coefficient as a weight to capture node similarity. Thirdly, we employ a community detection approach that unveils the community structure through a cuckoo search algorithm applied to the multiplex node embeddings to extract the best set of cluster centres by maximizing the modularity function. Experimental evaluations across synthetic and real-world multiplex networks attest to the efficiency and effectiveness of our approach, outperforming state-of-the-art algorithms.

Multi-relational community detection is a very important task in social network analysis because in the real world social networks are mostly multi-relational. In this paper, we propose a user-dependent method to detect communities in multi-relational social networks. We define a multi-relational community as a shared community over multiple single-relational graphs while the quality of a partitioning of nodes is assessed by a multi-relational modularity, and we design a desirable set of communities to represent the requests of users and use Normalized mutual information (NMI) between the desirable set of communities and the sets of communities detected in each single-relational graph as the weights for measuring the importance of all kinds of relationship types. We then use a greedy agglomerative manner to identify communities. Experiments have been conducted on synthetic networks to evaluate the effectiveness of the proposed approach.

Community detection in social media is a fundamental problem in social data analytics in order to understand user relationships and improve social recommendations. Although the problem has been extensively investigated, most of the research examined communities based on static structure in social networks. Our findings within large social networks such as Twitter, show that only a few users have interactions or communications within any fixed time interval. It is not difficult to see that it makes more potential sense to find such active communities that are biased to temporal interactions of social users, rather than relying solely on static structure. Communities detected with this new perspective will provide time-variant social relationships or recommendations in social networks, which can greatly improve the applicability of social data analytics.

Community detection in social networks based on fire propagation