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Realization of automatically extracting the role relation from massive unstructured data facilitates the deep semantics of mining big data. Nowadays, methods for network construction are mainly either based on structured data, or based on a single view. Therefore social network construction of role relation in unstructured data based on multi-view is still a challenge. In this paper, a method is proposed...
A number of approaches based on symmetric nonnegative matrix factorization (SNMF) have been proposed to improve the performance and the interpretability of community detection. Due to the nature of NMF, the partition results obtained by conventional NMF without post processing are soft assignments of nodes w.r.t. communities, which demonstrates overlapping of communities. Based on the traditional...
The team formation problem is required to find a group of individuals that can match the skills required by a collaborative task. Large-scale and comprehensive scientific research tasks need skilled experts from various fields to form a research team and work for it. This paper constructs a dataset and proposes team formation algorithms to find out research teams, which provides decision support for...
Community question answering(CQA) websites such as Yahoo! Answers and Stack Overflow provide a new way of asking and answering questions which are not well served by general web search engines. Due to the huge volume and ever-increasing number of questions, not all new questions can get fully answered in required time. Therefore, it is of great significance to design some effective strategies of recommending...
Community question answering(CQA) websites such as Quora and StackOverflow provide a new way of asking and answering questions which are not well served by general web search engines. With the huge volume and ever-increasing number of users and questions, effective strategies of ranking experts for different questions need to be proposed. In this paper, we first make some analysis on the network structure...
As a representation of information, Multi-dimension network is more and more popular, such as web data and social network. With the increment of data source, the entities of the network become diverse. How to analyze these multi-dimensional heterogeneous networks effectively and efficiently is a big challenge. In this paper, we propose a Two-Step Multi-dimensional Heterogeneous (TSMH) Graph Cube framework...
Much of the data of scientific interest, particularly when independence of data is not assumed, can be represented in the form of networks where data nodes are joined together to form edges corresponding to some kind of associations or relationships. Such information networks abound, like protein interactions in biology, web page hyperlink connections in information retrieval on the Web, cellphone...
Network structure has been used to describe many systems in the real world. Nodes, which represent the units, and edges, which represent the connection of the units constitute the network structure. The monitoring of the Peer-to-Peer (P2P) networks can also be described by the general network model. In this paper, we provide an algorithm to divide network based on the social network analysis. By calculating...
In this paper, we proposed a novel scheme to infer user's location using microblog text and friendships, without known geo information. The major part of our research is identifying local words, words that associated with some particular location. With local words we identified, we use conditional random fields (CRF), to detect location specific microblog. Then we can estimate the most possible location...
Community detection is one of the most important problems in social network analysis in the context of the structure of the underlying graphs. Many researchers have proposed their own methods for discovering dense regions in social networks. Such methods are only designed with links of the underlying social network. However, with the development of recent applications, rich edge content can be available...
The majority of real-world networks are directed, weighted and dynamic. Aiming at the problem of node role analysis in directed weighted network, in this paper, we introduce topological potential to network analysis and propose a novel node role analysis method based on directed topological potential, which can divide nodes into four kinds of roles based on their behavior pattern and local influence...
Currently, the detection of global community structure in networks has gathered a lot of attention. Most of the methods need global knowledge of the graphs which would be unrealistic to get when the graphs are too large or evolve too quickly. Moreover, sometimes we are only interested in the community structures of some given nodes, not all nodes. So detecting the community of a given node i.e. local...
Detecting communities plays a great important role in sociology, biology and computer science, disciplines where systems are often modeled as graphs. Such inherent community structures make us deeply understand about the networks and therefore have drawn significant interests among researchers. This paper describes a probabilistic community detection algorithm by modeling topic on sampling sub graphs...
Decentralized search in networks is an important algorithmic problem in the study of complex networks and graph mining. It has a large number of practical applications, including shortest paths search in social network relationship, web pages search in WWW, querying files in peer-to-peer file sharing networks and so on. In this paper, we present a probabilistic analysis of this search problem that...
The continued exponential growth in volume of literature data is giving birth to a new challenge to the bibliographic analysis service and the traditional features such as keyword search, author search and statistics services could not satisfy researchers for in-depth analysis. The emerging of community analysis in social networks is becoming a hot topic in many domains and disciplines such as sociology,...
With the rapid development of Information Technology such as Web 2.0, the internet is getting more interactive which supplying more information of individuals and social community composed of individuals for market analysis in enterprises. A new Social CRM Tool framework based on complex network analysis technologies aiming to offer better marketing and sale results for Mobile BOSS (Business Operation...
Community detection and tracking in social network is an important research area for many applications which are widely applied in complex systems. Recently there has been a surge of investigation in this area, fueled largely by interest in social networks, but also by interest in bibliographic citations and telecommunication records. However, due to the computational cost of the traditional algorithm...
The structure of customer communication network provides us the insights into the function of customers' relationships. In this paper, we use egocentric social network to explore how people manage their personal and group communications over time. Our primary goal is that our findings can provide business insights and help devise strategies for telecom service providers. We are interested in tracking...
An interesting property of network is that the information is not only contained in the entities, but also in the links between them. As the structure of the co-authorship network can greatly influence its function and reflect how the internal information is exchanged. We attempt to get deep insight of the features in a co-authorship network at a university. This is done by the following two steps...
As acquiring and retaining the most profitable customers are challenging tasks of service providers, various CRM tools are used to support these processes. Traditional CRM methods focus on various customer profitability models in different scenarios based on their past profit contribution. Social network analysis provides a natural way to understand the relationships between customers; however, this...
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