Put Three and Three Together

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.

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.

The problem of community detection in social media has been widely studied in the social networking community in the context of the structure of the underlying graphs. Most community detection algorithms use the links between the nodes in order to determine the dense regions in the graph. These dense regions are the communities of social media in the graph. Such methods are typically based purely on the linkage structure of the underlying social media network. However, in many recent applications, edge content is available in order to provide better supervision to the community detection process. Many natural representations of edges in social interactions such as shared images and videos, user tags and comments are naturally associated with content on the edges. While some work has been done on utilizing node content for community detection, the presence of edge content presents unprecedented opportunities and flexibility for the community detection process. We will show that such edge content can be leveraged in order to greatly improve the effectiveness of the community detection process in social media networks. We present experimental results illustrating the effectiveness of our approach.

Fuzzy-rough community in social networks

This thesis focuses on the pressing issue of illicit drug trafficking and its impact on public health and safety at a global level. With the advent of digital technologies and social media platforms, combating drug trafficking has become increasingly challenging for law enforcement and researchers alike. Among these platforms, Instagram, a popular photo and video-sharing social networking platform, has emerged as a prominent hub for drug trafficking activities. In this study, we delve into the effectiveness of community and key player detection algorithms in identifying and disrupting illicit drug supply networks on Instagram. To conduct our research, we collected real Instagram data spanning from June to August 2022. We examined several community detection algorithms, including Louvain, Newman-Girvan, Infomap, Label Propagation, and Hierarchical Clustering. Additionally, we explored key player algorithms, namely CDKPE, TopRank, K-core, and KPEI, the latter being a novel algorithm introduced in this study. Our objective was to assess the performance of these algorithms in accurately identifying and targeting drug trafficking networks. Our findings reveal that the Louvain and Newman-Girvan algorithms outperformed others in terms of community detection, demonstrating their effectiveness in identifying cohesive groups involved in drug trafficking on Instagram. In terms of key player detection, the CDKPE and KPEI algorithms emerged as the most effective, highlighting the individuals who play pivotal roles within these networks. These algorithms offer practical applications for law enforcement agencies seeking to disrupt drug trafficking operations on Instagram. By emphasizing the importance of leveraging advanced analytical tools, our study underscores the significance of combating drug trafficking on social media platforms.

How to efficiently detect communities for dynamic graphs has attracted significant attention due to its widespread applications, such as "Recommended System" in social networking or "Anti-Money Laundry" for banks. However, with the growing size and the increasing of changing frequency for dynamic graphs, it is better to detect communities incrementally than applying community detection algorithm for each of the graph. In this paper, we propose a generalized bottom-up community detection framework to help standard community detection algorithms to detect communities in highly dynamic graphs incrementally. The evaluations on real data sets show that our generalized framework does have the ability to accelerate standard community detection methods for highly dynamic graphs (up to 96%). In addition, for super hubs in large scale graph, we also propose an approximate process to meet the requirement of real-time processing. By giving "mathematical proof", "efficiency" and "accuracy" evaluations from real data sets, we demonstrate this approximate process can not only accelerate the community detection process but also can preserve the accuracy, as the normalized mutual information for regular communities and approximate communities is larger than 92%.

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.

Many real world systems or web services can be represented as a network such as social networks and transportation networks. In the past decade, many algorithms have been developed to detect the communities in a network. However, the impact of locations on community has not been fully investigated by the research literature. In this paper, we propose a method to determine if a location-based community detection method is suitable for a given network and provide a new community detection algorithm that pushes the location information into the community detection. We test our proposed method on both synthetic data and real world network datasets. The results show that the communities detected by our method distribute in a smaller area compared with the traditional methods and have the similar or higher tightness on network connections.

Community detection in social networks based on fire propagation

Assorted communication on social networks attracted researcher's attention in recent time. Massive sharing of opinions, ideas, experiences and expertise highlight communication through social networks. Social networks have becoming a flourishing network for sharing such values. Identifying and analyzing these communication trends have gained importance for detecting patterns among peoples in social networks using community detection. Different methods have been proposed for detecting communities in social networks. Co-authorship network is also becoming central point of attention to many researchers. Domain specific co-authorship community detection is evolving area that is emerging beside other community detection identification patterns. In this paper, we performed analysis on coauthorship network in the field of computer networks using common centrality measures. Finding reveals that this study finds domain specific co-authors that are best in their knowledge sharing and collaboration in the field of computer networks.

During the last decade, the social networks have known a huge popularity due to their ease of connecting people. The community detection has been in the center of attention in the analysis of this kind of networks. However, this area is still a very active field of research; the majority of methods involving this problem suffers from the accuracy in the determination of meaningful modules or the computational complexity of the used algorithm. In this paper, our contribution is to propose a new method that reveals the communities in social networks based on an excellent similarity measure and the minimum spanning tree. Our approach guaranties both accuracy and efficiency of the resulted partitions when compared with the existing methods and considering five real world social networks with a very promising computational complexity.

The research on communities in social networks takes many paths in the literature, among which: the problematic of accurately detecting communities; modeling the evolution of those communities within the evolving network; and then finding the patterns that characterize this evolution over time. In our work, we focused on the problematic of detecting communities in social networks based on the information disseminated among users of the social network and the type of content shared by these users. The work at hand consists of a brief introduction to the subject and the problem definition, then we move to state the main contribution of our work which consists of a multi-layer model to detect communities of users based on the content shared by users, the lowest layer would detect topics of interest of each user while the upper layer would form communities from generated topics. We conclude the paper stating our perspectives and future works.

A cascade information diffusion based label propagation algorithm for community detection in dynamic social networks

Communities are inherent substructures present in social networks. Yet finding communities from a social network can be a difficult task. Therefore, finding communities from a social network is an interesting problem. Also, due to its use in many practical applications, it is considered to be an important problem in social network analysis and is well-studied. In this paper, we propose a maximum spanning tree based method to detect communities from a social network. Experimental results show that this method can detect communities with high accuracy and with reasonably good efficiency compared to other existing community detection techniques.KeywordsSocial NetworksCommunity DetectionMaximum Spanning Tree

Incorporating Community Detection and Clustering Techniques into Collaborative Filtering Model