Community detection is an important tool for analyzing the social graph of mobile phone users. The problem of nding communities in static graphs has been widely studied. However, since mobile social networks evolve over time, static graph algorithms are not sucient. To be useful in practice (e.g. when used by a telecom analyst), the stability of the partitions becomes critical. We tackle this particular use case in this paper: tracking evolution of com- munities in dynamic scenarios with focus on stability. We propose two modications to a widely used static community detection algorithm: we introduce xed nodes and preferential attachment to pre-existing com- munities. We then describe experiments to study the stability and quality of the resulting partitions on real-world social networks, represented by monthly call graphs for millions of subscribers.
Many related papers have investigated the importance of communities, and served us as reference. Foundational concepts and examples for communities detection may be found in @cite_0 . For instance, the interactions between the major characters of the novel Les Misérables'' by Victor Hugo can be viewed as a graph with 77 nodes, and the characters organized as communities (the example is very intuitive and easy to grasp for the fans of the novel). Another classic example is Zachary’s karate club, an organization with 34 nodes that split into two separate clubs in real life. Each new part can be mapped almost perfectly to the two main communities of friends detected in the original club. The paper also analyzes algorithms, such as shortest-path and random walk. Of course, the graphs considered in @cite_0 have a much smaller scale than the ones considered in this work.
@cite_0
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Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system’s components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks’ links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures. Recently community detection has attracted much interest in social media to understand the collective behaviours of users and allow individuals to be modeled in the context of the group. Most existing approaches for community detection exploit either users' social links or their published content, aiming at discovering groups of densely connected or highly similar users. They often fail to find effective communities due to excessive noise in content, sparsity in links, and heterogenous behaviours of users in social media. Further, they are unable to provide insights and rationales behind the formation of the group and the collective behaviours of the users. To tackle these challenges, we propose to discover communities in a low- dimensional latent space in which we simultaneously learn the representation of users and communities. In particular, we integrated different social views of the network into a low-dimensional latent space in which we sought dense clusters of users as communities. By imposing a Laplacian regularizer into affiliation matrix, we further incorporated prior knowledge into the community discovery process. Finally community profiles were computed by a linear operator integrating the profiles of members. Taking the wellness domain as an example, we conducted experiments on a large scale real world dataset of users tweeting about diabetes and its related concepts, which demonstrate the effectiveness of our approach in discovering and profiling user communities. Recent years have seen that WWW is becoming a flourishing social media which enables individuals to easily share opinions, experiences and expertise at the push of a single button. With the pervasive usage of instant messaging systems and the fundamental shift in the ease of publishing content, social network researchers and graph theory researchers are now concerned with inferring community structures by analyzing the linkage patterns among individuals and web pages. Although the investigation of community structures has motivated many diverse algorithms, most of them are unsuitable for large-scale social networks because of the computational cost. Moreover, in addition to identify the possible community structures, how to define and explain the discovered communities is also significant in many practical scenarios. In this paper, we present the algorithm ComTector(Community DeTector) which is more efficient for the community detection in large-scale social networks based on the nature of overlapping communities in the real world. This algorithm does not require any priori knowledge about the number or the original division of the communities. Because real networks are often large sparse graphs, its running time is thus O(C × Tri2), where C is the number of the detected communities and Tri is the number of the triangles in the given network for the worst case. Then we propose a general naming method by combining the topological information with the entity attributes to define the discovered communities. With respected to practical applications, ComTector is challenged with several real life networks including the Zachary Karate Club, American College Football, Scientific Collaboration, and Telecommunications Call networks. Experimental results show that this algorithm can extract meaningful communities that are agreed with both of the objective facts and our intuitions. Community detection is a common problem in graph data analytics that consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. In this review, we evaluated eight state-of-the-art and five traditional algorithms for overlapping and disjoint community detection on large-scale real-world networks with known ground-truth communities. These 13 algorithms were empirically compared using goodness metrics that measure the structural properties of the identified communities, as well as performance metrics that evaluate these communities against the ground-truth. Our results show that these two types of metrics are not equivalent. That is, an algorithm may perform well in terms of goodness metrics, but poorly in terms of performance metrics, or vice versa. © 2014 The Authors. WIREs Computational