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Modeling of data is an important step in process of interpreting the data and to understand the desired situation more clearly. The topic of social network structures is one of the highly studied subject and modeling is very important for social network mining. One of the modeling tools for such structures is Graphs. Graphs have been used for modeling and visualization tool of many structures such...
In the algorithm of community detection using clustering technology, the prior information of community structure and the similarity measure between nodes influence the clustering effect greatly. For how to adaptively discover the community and utilize the network topology to calculate the similarity between nodes in order to raise the modularity of network, the community detection algorithm based...
One of the traditional ways for detecting dynamic communities is to find the communities at each interval through the static community detection algorithms. However, it usually leads to high computation complexity. In this paper, a novel algorithm based on the MapReduce model and the label propagation progress with the strategy of incremental related vertices is proposed, which is called PLPIRV (Parallel...
A persona in a social network is defined as the person's activities and attributes in a social network as seen by others. And a community in a social network is defined as a group of users in that social network which share common interests and are most likely to interact with each other in the network. For community detection, a user's persona and its connections with the other users in a network,...
Many phenomena in our world can be modeled as networks, from neurons in the human brain, computer networks and bank transactions to social interactions. Anomaly detection is an important data mining task consisting in detecting rare objects that deviate from the majority of the data. Contextual collective anomaly detection techniques can be applied to intrusion detection in computer networks, bank...
As the detection of social circles can help the users find the other users with similar interests in a big data environment to expand their friend circles, our algorithm takes the (implicit) user topic in micro-blog and the (explicit) follow relationship between the users into comprehensive account. Firstly, use the supervised-LDA model to extract user topics from micro-blogging data and calculate...
Community detection is one of the most important ways that reflect the structure and mechanism beneath the social network. The overlapping communities are more in line with the reality of social network. In the society, the phenomenon of some members shared membership of different communities reflects as overlapping communities in the network. Facing big data network, it is a challenging and computationally...
Nowadays, the emergence of online social networks have empowered people to easily share information and media with friends. Interacting users of social networks with similar users and their friends form community structures of networks. Uncovering communities of the online users in social networks plays an important role in network analysis with many applications such as finding a set of expert users,...
We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides...
The evolution of web technology has caused huge amounts of data to be available on the internet, and especially in social networks. Data mining is used in helping to extract valuable information from large amounts of data. One problem of great interest in mining social networks is community detection which can be understood as the unsupervised discovery of densely connected subgroups within the network...
Community structure is one of the most important features of real networks and it can reveal the internal organization of the nodes. The classical GN algorithm obtains communities by iterative computation of global betweenness in a social network so it can only get a course-grained community partition. In this paper we proposed Co-game, a community overlapping detection algorithm based on game theory...
In recent years community detection has been a hot research topic in network science, which helps to explain the characteristics of the network structure. This paper analyzed the effect of vertices with high influence in community detection, and found that such vertices have different roles in different networks. A variable influence community detection algorithm based on PageRank is proposed in this...
The conventional algorithm (COPRA -- Community Overlap PRopagation Algorithm) proposed by Steve Gregory is efficient and useful in Complex Networks, but it is a challenge to select a suitable parameter "thr" as the input of the algorithm. In this paper, we put forward a threshold based label propagation algorithm, in which each vertex in the network is identified with a threshold respectively,...
Community detection is an effective tool for mining hidden information in social networks. Label propagation algorithms (LPA) have been proved to be very fast, which do not require prior information e.g., the number and the size of the communities. However, the results of these algorithms are random and not stable. In this paper, a novel random-walk based label propagation community detection algorithm...
In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and in particular interest groups. Identifying these users' communities and the interests that bind them can help us assist their life-cycle. Certain kinds of online communities such as question-and-answer (Q&A) sites or forums, have no explicit...
In this paper we propose two algorithms for overlapping community detection based on neighborhood vector propagation algorithm(NVPA), a community detection algorithm which can detect disjoint communities with high accuracy. The first algorithm is named Link Partition of Overlapping Communities (LPOC). In this algorithm, we first convert a node graph to a link graph, then we use NVPA to find the communities...
Mobile social network is a type of delay tolerant network of mobile devices in which there is no end-to-end path available in advance for communication. It works on the principle of a store-carry-forward mechanism. The community is a very useful property of the mobile social network as humans are social animals and they like to live in a community. Such community structure enables efficient communication...
Community detection is a fundamental problem for many networks, and there have been a lot of methods proposed to discover communities. However, with the rapid increase of the scale and diversity of networks, only a few methods can handle large networks with overlaps among communities. Detecting communities from the local views of a small number of seed nodes is one of the successful methods which...
Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect communities which are shared by all layers. In this article we propose a community detection...
The proliferation of social networking sites and their rapidly growing user-base have made information sharing simpler than ever before. However, a typical social network user might get easily overloaded with information that may not be of interest to the user. Further the number of users and the links which they exhibit among their peers are very huge in a typical social network. Hence, identifying...
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