Employees in companies can be divided into di erent communities, and those who frequently socialize with each other will be treated as close friends and are grouped in the same community. In the enterprise context, a large amount of information about the employees is available in both (1) o ine company internal sources and (2) online enterprise social networks (ESNs). Each of the information sources also contain multiple categories of employees' socialization activities at the same time. In this paper, we propose to detect the social communities of the employees in companies based on the broad learning se ing with both these online and o ine information sources simultaneously, and the problem is formally called the "Broad Learning based Enterprise Community Detection" (BL-Ecd) problem. To address the problem, a novel broad learning based community detection framework named "HeterogeneoUs Multi-sOurce ClusteRing" (Humor) is introduced in this paper. Based on the various enterprise social intimacy measures introduced in this paper, Humor detects a set of micro community structures of the employees based on each of the socialization activities respectively. To obtain the (globally) consistent community structure of employees in the company, Humor further fuses these micro community structures via two broad learning phases: (1) intra-fusion of micro community structures to obtain the online and o ine (locally) consistent communities respectively, and (2) inter-fusion of the online and o ine communities to achieve the (globally) consistent community structure of employees. Extensive experiments conducted on real-world enterprise datasets demonstrate our method can perform very well in addressing the BL-Ecd problem.
@cite_18: Social networks have been part of people's daily life and plenty of users have registered accounts in multiple social networks. Interconnections among multiple social networks add a multiplier effect to social applications when fully used. With the sharp expansion of network size, traditional standalone algorithms can no longer support computing on large scale networks while alternatively, distributed and parallel computing become a solution to utilize the data-intensive information hidden in multiple social networks. As such, synergistic partitioning, which takes the relationships among different networks into consideration and focuses on partitioning the same nodes of different networks into same partitions. With that, the partitions containing the same nodes can be assigned to the same server to improve the data locality and reduce communication overhead among servers, which are very important for distributed applications. To date, there have been limited studies on multiple large scale network partitioning due to three major challenges: 1) the need to consider relationships across multiple networks given the existence of intricate interactions, 2) the difficulty for standalone programs to utilize traditional partitioning methods, 3) the fact that to generate balanced partitions is NP-complete. In this paper, we propose a novel framework to partition multiple social networks synergistically. In particular, we apply a distributed multilevel k-way partitioning method to divide the first network into k partitions. Based on the given anchor nodes which exist in all the social networks and the partition results of the first network, using MapReduce, we then develop a modified distributed multilevel partitioning method to divide other networks. Extensive experiments on two real data sets demonstrate that our method can significantly outperform baseline independent-partitioning method in accuracy and scalability.
@cite_28: With the recent advances in information networks, the problem of community detection has attracted much attention in the last decade. While network community detection has been ubiquitous, the task of collecting complete network data remains challenging in many real-world applications. Usually the collected network is incomplete with most of the edges missing. Commonly, in such networks, all nodes with attributes are available while only the edges within a few local regions of the network can be observed. In this paper, we study the problem of detecting communities in incomplete information networks with missing edges. We first learn a distance metric to reproduce the link-based distance between nodes from the observed edges in the local information regions. We then use the learned distance metric to estimate the distance between any pair of nodes in the network. A hierarchical clustering approach is proposed to detect communities within the incomplete information networks. Empirical studies on real-world information networks demonstrate that our proposed method can effectively detect community structures within incomplete information networks.
@cite_29: Nowadays, many new social networks offering specific services spring up overnight. In this paper, we want to detect communities for emerging networks. Community detection for emerging networks is very challenging as information in emerging networks is usually too sparse for traditional methods to calculate effective closeness scores among users and achieve good community detection results. Meanwhile, users nowadays usually join multiple social networks simultaneously, some of which are developed and can share common information with the emerging networks. Based on both link and attribution information across multiple networks, a new general closeness measure, intimacy, is introduced in this paper. With both micro and macro controls, an effective and efficient method, CAD (Cold stArt community Detector), is proposed to propagate information from developed network to calculate effective intimacy scores among users in emerging networks. Extensive experiments conducted on real-world social networks demonstrate that CAD can perform very well in addressing the emerging network community detection problem.
@cite_6: With the rapid development of online social media, online shopping sites and cyber-physical systems, heterogeneous information networks have become increasingly popular and content-rich over time. In many cases, such networks contain multiple types of objects and links, as well as different kinds of attributes. The clustering of these objects can provide useful insights in many applications. However, the clustering of such networks can be challenging since (a) the attribute values of objects are often incomplete, which implies that an object may carry only partial attributes or even no attributes to correctly label itself; and (b) the links of different types may carry different kinds of semantic meanings, and it is a difficult task to determine the nature of their relative importance in helping the clustering for a given purpose. In this paper, we address these challenges by proposing a model-based clustering algorithm. We design a probabilistic model which clusters the objects of different types into a common hidden space, by using a user-specified set of attributes, as well as the links from different relations. The strengths of different types of links are automatically learned, and are determined by the given purpose of clustering. An iterative algorithm is designed for solving the clustering problem, in which the strengths of different types of links and the quality of clustering results mutually enhance each other. Our experimental results on real and synthetic data sets demonstrate the effectiveness and efficiency of the algorithm.
@cite_3: We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We applied this approach to segmenting static images, as well as motion sequences, and found the results to be very encouraging.
@cite_0: We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Clustering method has also been widely used to detect communities in networks. introduce a modularity function measuring the quality of a division of networks @cite_0 . introduce the concept of normalized cut and discover that the eigenvectors of the Laplace matrix provide a solution to the normalized cut objective function @cite_3 . In addition, many community detection works have been done on heterogeneous online social networks. @cite_6 propose to study the clustering problem with complete link information but incomplete attribute information. @cite_28 try to detect the communities in networks with incomplete relational information but complete attribute information. In recent years, many works have propose to detect communities across aligned networks. @cite_29 propose to detect the communities in emerging networks with extremely sparse relational information and little attribute information by propagating information from other aligned networks (with abundant of information). @cite_18 propose to study the synergistic partition problem of multiple large scale partially aligned social networks based on the Map-Reduce.