Community Discovery Algorithm for Social Networks Based on Parallel Recommendation

In view of that most of the current community discovery methods in social network do not consider node self-transfer and node bias, so that it is not possible to extract the graph features effectively, which leads to the ineffective problem by the community discovery, this paper proposes a novel social network community discovery algorithm (Local Distance Laplace, LDL). First, a Laplace matrix decomposition model is constructed based on the principle of matrix decomposition. Secondly, considering the high cost of global social network information acquisition and calculation, a community discovery model based on local distance is proposed. Finally, the optimal community structure is selected by using the NRO (Node Rank Optimization) function. A comprehensive comparative analysis is made on eleven real and synthetic networks. At the same time, validation analysis is conducted on eleven different social networks (Karate, Dolphins, Lemis, Public book, Football, Celegansnertal, Email, Public blogs, Netscience, Power, Hep_th). The experimental simulation results show that: in the real network, the proposed LDL algorithm improved the overall performance by nearly 7% compared with the seven state of art optimal methods (CoVeC, EDBC, JNMF, EADP, LPANNI, LSA, SCFS). The novel algorithm is reasonable and effective and it can also be extended to multi-scale community discovery.

The objective of this study is to discover social communities in a social network using different social network community discovery methods that utilize metrics and structures like degree, clustering coefficient, k-cores, weak and strong components. We have used two different datasets and methods: K-core community discovery method for DBLP dataset and Main Path Analysis method for Arxiv High-energy physics theory citation network. At the end of the analyses, we have obtained several reports that represent the skeleton structure of the communities in the networks.

The article is devoted to the topical issue of identifying information threats in virtual communities of social networks and mechanisms for counteracting such threats. The process of organization of monitoring and content analysis of virtual communities in social networks is studied. The technology of detecting information threats of virtual communities in social networks and methods of their assessment have been developed. An approach to finding pages of discussions on social networks based on the Louvain method is proposed. The technology of determining the degree of information threat of the virtual community in social networks and ways to counteract the information threats of the virtual community have been developed.

Aims: To examine the association of social networks and community engagement with cognitive impairment among community-dwelling Chinese older adults. Methods: From November 2017 to May 2018, we selected 1,115 elderly individuals from 3 Chinese communities (Beijing, Hefei, and Lanzhou) using a random-cluster sampling method, and recorded data on demographics, social network characteristics, community activities, and cognitive function. The odds ratios (ORs) of these associations were adjusted for potential confounders in logistic regression models. Results: The prevalence of cognitive impairment was 25.7% (n = 287). An adequate social network (OR 0.55; 95% confidence interval [CI] 0.33–0.91) and enough social support from friends (OR 0.43; 95% CI 0.29–0.62) were negatively associated with cognitive impairment. Family support was not significantly associated with cognitive impairment (OR 0.64; 95% CI 0.34–1.21). Taking part in elderly group travel, communication with others using WeChat, and community activities such as Tai Chi and walking together were negatively associated with cognitive impairment. Conclusion: Social network characteristics and community engagement were found to be related to cognitive function among community-dwelling Chinese elderly adults.

This chapter explores the topic of virtual community discovery in social networks. It starts with defining virtual communities. After that, the chapter goes over several network modelling strategies and community identification techniques. The significance of standardised datasets in evaluating algorithm performance is shown by a thorough analysis of benchmark networks used to test community detection techniques. The accuracy indices for assessing virtual community identification algorithms are also covered. The chapter offers common datasets needed to evaluate virtual community discovery techniques. Next, it explores community discovery using the Girvan-Newman and Louvain algorithms, two well-known techniques, to demonstrate practical applications. These illustrations explain how theoretical ideas can be applied in practical settings. Finally, this chapter provides a thorough overview of the concepts and assessment criteria for virtual community discovery in social networks, providing insightful information for both scholars and industry professionals.

The conventional notion of community that favors a high ratio of internal edges to outbound edges becomes invalid when each vertex participates in multiple communities. Such a behavior is commonplace in social networks. The significant overlaps among communities make most existing community detection algorithms ineffective. The lack of effective and efficient tools resulted in very few empirical studies on large-scale detection and analyses of overlapping community structure in real social networks. We developed recently a scalable and accurate method called the Partial Community Merger Algorithm (PCMA) with linear complexity and demonstrated its effectiveness by analyzing two online social networks, Sina Weibo and Friendster, with 79.4 and 65.6 million vertices, respectively. Here, we report in-depth analyses of the 2.9 million communities detected by PCMA to uncover their complex overlapping structure. Each community usually overlaps with a significant number of other communities and has far more outbound edges than internal edges. Yet, the communities remain well separated from each other. Most vertices in a community are multi-membership vertices, and they can be at the core or the peripheral. Almost half of the entire network can be accounted for by an extremely dense network of communities, with the communities being the vertices and the overlaps being the edges. The empirical findings ask for rethinking the notion of community, especially the boundary of a community. Realizing that it is how the edges are organized that matters, the f-core is suggested as a suitable concept for overlapping community in social networks. The results shed new light on the understanding of overlapping community.

Communities in social networks form with different purposes and play a significant role in interpersonal interactions. Analysis of virtual communities indicates a more precise understanding of the behaviours and desires of individuals in social networks. In this paper, new measures have been proposed for analyzing implicit and explicit communities in Online Social Networks (OSNs). The measures of “potential value of the community members” and “value of the community messages”, which are used for calculating the measure of “community value” are among the most important measures introduced in this paper. Another measure introduced is “user influence rate” in a community, which represents the contribution of a person in creating value in a community. To provide a sound dataset, we collected the information from several real implicit communities in Twitter based on different hashtags. Finally, the suggested measures have been analyzed and compared statistically and behaviourally across different communities. The results of this research well indicate the importance and practicality of the measures introduced in Community analysis of Twitter.

Modern transportation has given rise to and facilitated the movement and mobility of populations. While much critical attention has been drawn to the permanent migration of the mobile population, very few scholars in the field of sociolinguistics have stressed the temporary movement of the group of travelers who are subsumed under the appellation, “the sojourner”. In addition, previous research predominantly focused on non-English speakers sojourning to English-dominant countries (Haneda and Monobe, 2009; Lee, 2008; Own, 1999), thus largely neglecting the multilingual contexts in Asia. To fill this scholarly gap, small-scale research was conducted by employing two frameworks, Social Network Theory (Milroy, 1980) and Community of Practice (Wenger, 1998; Eckert & McConnell-Ginet, 1992), to examine linguistic practices and identity positioning of Southeast Asian (SEA) sojourners when they interact with Hongkongers and other sojourners. In particular, it tackles a more complex language contact situation in which two major lingua francas, namely English and Mandarin, are available. This study was conducted in a higher education dance school in Hong Kong and three SEA sojourn students were recruited. Multi-faceted identities and multiple communities of practices are found: (1) at Communal Level: Cantonese is the shared linguistic repertoire of this dance community and three SEA sojourn students form the weakest ties with local students; (2) at Group Level: both Mandarin and English are adopted and stronger ties are established with other overseas sojourn students; and (3) at Individual Level: Singapore English is the dominant code choice used among these three SEA sojourners and they are bound together by the strongest ties. Instances of trilingual code-mixing and code-switching are also found in the interaction among the sojourners and Hong Kong locals. The two theories, Social Network Theory and Community of Practice, are complementary in accounting for the social organization of multilingual communities. Ultimately, this study demonstrates the complexity of multilingual communities with the aforesaid language contact in Hong Kong as a case in point.

Laboratory studies of social behavior have typically focused on dyadic interactions occurring within a limited spatiotemporal context. However, this strategy prevents analyses of the dynamics of group social behavior and constrains identification of the biological pathways mediating individual differences in behavior. In the current study, we aimed to identify the spatiotemporal dynamics and hierarchical organization of a large social network of male mice. We also sought to determine if standard assays of social and exploratory behavior are predictive of social behavior in this social network and whether individual network position was associated with the mRNA expression of two plasticity-related genes, DNA methyltransferase 1 and 3a. Mice were observed to form a hierarchically organized social network and self-organized into two separate social network communities. Members of both communities exhibited distinct patterns of socio-spatial organization within the vivaria that was not limited to only agonistic interactions. We further established that exploratory and social behaviors in standard behavioral assays conducted prior to placing the mice into the large group was predictive of initial network position and behavior but were not associated with final social network position. Finally, we determined that social network position is associated with variation in mRNA levels of two neural plasticity genes, DNMT1 and DNMT3a, in the hippocampus but not the mPOA. This work demonstrates the importance of understanding the role of social context and complex social dynamics in determining the relationship between individual differences in social behavior and brain gene expression.

The increasing size and complexity of online social networks have brought distinct challenges to the task of community discovery. A community discovery algorithm needs to be efficient, not taking a prohibitive amount of time to finish. The algorithm should also be scalable, capable of handling large networks containing billions of edges or even more. Furthermore, a community discovery algorithm should be effective in that it produces community assignments of high quality. In this chapter, we present a selection of algorithms that follow simple design principles, and have proven highly effective and efficient according to extensive empirical evaluations. We start by discussing a generic approach of community discovery by combining multilevel graph contraction with core clustering algorithms. Next we describe the usage of network sampling in community discovery, where the goal is to reduce the number of nodes and/or edges while retaining the network’s underlying community structure. Finally, we review research efforts that leverage various parallel and distributed computing paradigms in community discovery, which can facilitate finding communities in tera- and peta-scale networks.

With the rapid development of various social platforms, social networks have accumulated more and more data. These huge and complex data contain valuable information in many fields. It is of far-reaching significance to conduct statistical analysis of these data and dig out valuable information and apply it in real life. Based on the homogeneity of social networks, our paper proposes a framework of discovery algorithms based on the homogeneity of microblogs to realize the theme community discovery of microblog social networks. Firstly, the data set is preprocessed, the invalid data is removed and classified by microblog users. Secondly, the LDA model is used to extract the topic of microblog user blogs to describe the characteristics of microblog users. Thirdly, two users are calculated based on the theme interest. The homogeneity measure between them is used to represent the relationship of social networks. Finally, the community of the implicit social network is realized through unsupervised algorithms to build communities with the same interest as the theme. In order to verify the validity of our proposed algorithm framework, this paper uses data sets from microblog network to conduct experiments. The experimental results show that the algorithm effectively combines the homogeneity measure of the network and has achieved very good results in the implicit community discovery.

Chapter 1 - Why Design to Thrive?

: Current approaches to community detection in social networks often ignore the spatial location of the nodes. In this paper, we look to extract spatially-near communities in a social network. We introduce a new metric to measure the quality of a community partition in a geolocated social networks called spatially-near modularity a value that increases based on aspects of the network structure but decreases based on the distance between nodes in the communities. We then look to find an optimal partition with respect to this measure - which should be an ideal community with respect to both social ties and geographic location. Though an NP-hard problem, we introduce two heuristic algorithms that attempt to maximize this measure and outperform nongeographic community finding by an order of magnitude. Applications to counter-terrorism are also discussed.

Social networks are ubiquitous in our daily life. Due to the rapid development of information and electronic technology, social networks are becoming more and more complex in terms of sizes and contents. It is of paramount significance to analyse the structures of social networks in order to unveil the myth beneath complex social networks. Network community detection is recognised as a fundamental tool towards social networks analytics. As a consequence, numerical community detection methods are proposed in the literature. For a real-world social network, an individual may possess multiple memberships, while the existing community detection methods are mainly designed for non-overlapping situations. With regard to this, this paper proposes a hybrid metaheuristic method to detect overlapping communities in social networks. In the proposed method, the overlapping community detection problem is formulated as an optimisation problem and a novel bat optimisation algorithm is designed to solve the established optimisation model. To enhance the searchability of the proposed algorithm, a local search operator based on tabu search is introduced. To validate the effectiveness of the proposed algorithm, experiments on benchmark and real-world social networks are carried out. The experiments indicate that the proposed algorithm is promising for overlapping community detection.

The multi-relational social network is a new social network, which is proposed in recent years and more complex than single relational social network. Therefore, the existing community discovery algorithm is lack and their results are crude. The paper proposes a new algorithm for discovering community in multi-relational social network based on modified common neighbors similarity. The core idea is the more common neighbors are shared between two nodes, the more similar the nodes are. Finally, we test our generalized algorithm on an artificial sparse network in four dimensions.