Junyuan Xie

Overlapping Community Detection via Leader-Based Local Expansion in Social Networks

Most community detection algorithms are trying to obtain the global information of the network. But increasingly large scale of the current network makes accessing to global information very difficult. In the meanwhile, the network shows power-law distribution and sparse features. And local community mining algorithms which use these features have more advantages over global mining methods. In this paper, we proposed a local community detection algorithm based on the core members named LLCDA (Leader based Local Community Detecting Algorithm) which uses local structural information in the network to optimize a local objective function. A local community can be detected through continuous optimization of the function by expanding from an initial core member computed by a modified PageRank sorting algorithm. The proposed LLCDA algorithm has been tested on both synthetic and real world networks, and it has been compared with other community detecting algorithms. The experimental results validated our proposed LLCDA and showed that significant improvements have been achieved by this technique.

A Center-Based Community Detection Method in Weighted Networks

The study of community detection has received more and more attention in recent years, the problem is very difficult and of great importance in many fields such as sociology, biology and computer science. But most of the algorithms proposed so far could not utilize the weight information within weighted networks, and many of them are so time-consuming that they are not fit for the large-scale networks. We propose a new center-based method, which is especially designed for weighted networks. And the method is also suitable for large-scale network because of its low computational complexity. We demonstrate our method on a synthetic network and two real-world networks. The result shows the high efficiency and precision of our method.

Detecting Link Communities Based on Local Approach

Detecting communities from networks has been given great attention these years. The traditional approaches were always focusing on the node community, while some recent studies have shown great advantage of link community approach which partitions links instead of nodes into communities. We proposed a novel algorithm LBLC (local based link community) to detect link communities in the network based on local information. A local link community can be detected by maximizing a local link fitness function from a seed link, which was ranked by another algorithm previously. The proposed LBLC algorithm has been tested on both synthetic and real world networks, and it has been compared with other link community detecting algorithm. The experimental results showed LBLC achieves significant improvement on link community structure.

Entropy chain multi-label classifiers for traditional medicine diagnosing Parkinson's disease

Parkinson disease is a chronic, degenerative disease of the central nervous system, which commonly occurs in the elderly. Until now, no treatment has shown efficacy. Traditional Chinese Medicine is a new way for Parkinson, and the data of Chinese Medicine for Parkinson is a multi-label dataset. Classifier Chains(CC) is a popular multi-label classified algorithm, this algorithm considers the relativity between labels, and contains the high efficiency of Binary classification algorithm at the same time. But CC algorithm does not indicate how to obtain the predicted order chain actually, while more emphasizes the randomness or artificially specified. In this paper, we try to apply Multi-label classification technology to build a model of Chinese Medicine for Parkinson, which we hope to improve this field. We propose a new algorithm ETCC based on CC model. This algorithm can optimize the order chain on global perspective and have a better result than the algorithm CC.

Community detection for emerging social networks

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.

Inferring Social Influence of anti-Tobacco mass media campaigns

Anti-tobacco mass media campaigns are designed to influence tobacco users. It has been proved campaigns will produce their changes in awareness, knowledge, and attitudes, and also produce meaningful behavior change of audience. Anti-smoking television advertising is the most important part in the campaign. Meanwhile nowadays successful online social networks are creating new media environment, however little is known about the relation between social conversations and anti-tobacco campaigns. This paper aims to infer social influence of these campaigns, and the problem is formally referred to as the “Social Influence inference of anti-Tobacco mass mEdia campaigns” (SITE) problem. To address the SITE problem, a novel influence inference framework, “TV Advertising Social Influence Estimation” (ASIE), is proposed based on our analysis of two anti-tobacco campaigns. ASIE divides audience attitudes towards TV ads into three distinct stages: (1) Cognitive, (2) Affective and (3) Conative. Audience online reactions at each of these three stages are depicted by ASIE with specific probabilistic models based on the synergistic influences from both online social friends and offline TV ads. Extensive experiments demonstrate the effectiveness of ASIE.

Multi-label Classification: Dealing with Imbalance by Combining Labels

Data imbalance is a common problem both in single-label classification (SLC) and multi-label classification (MLC). There is no doubt that the predicting result suffers from this problem. Although, a broad range of studies associate with imbalance problem, most of them focus on SLC and for MLC is relatively less. Actually, this problem arising in MLCis more frequent and complex than in SLC. In this paper, we proceed from dealing with imbalance problem for MLC and propose a new approach called DEML. DEML transforms the whole label set of multi-label dataset into some subsets and each subset is treated as a multi-class dataset with balanced class distribution, which not only addressing imbalance problem but also preserving dataset integrity and consistency. Extensive experiments show that DEML possesses highly competitive performance both in computation and effectiveness.

A Spin-Glass Model Based Local Community Detection Method in Social Networks

Mining community structures has become a general problem which exists in many fields including: Computer-Science, Mathematics, Physics, Biology, Sociology and so on. It has developed rapidly and been used widely in many applications: web data mining, social network analysis, criminal network mining, protein interaction network analysis, metabolic network analysis, genetic network analysis, customers relationship mining and user online behavior analysis, etc. Most community detection algorithms try to obtain the global information of the network, but increasing large scale of the current network makes it computationally expensive. In the meanwhile, the different influence and different behavior of nodes in the network are ignored. In fact, if we know the local information of the network or the interested node, we can easily detect the local community. This paper proposes a multi-resolution local community detection algorithm named MRCDA which uses local structural information in the network to optimize the multi-resolution modularity based on the Potts spin-glass model. A local community can be detected through continuous optimization of the function by expanding from an initial influential node computed by a modified PageRank sorting algorithm. The proposed MRCDA has been tested on both synthetic and real world networks and tested against other algorithms. The experiments demonstrate its efficiency and accuracy.

An Algorithm for Mining Top K Influential Community Based Evolutionary Outliers in Temporal Dataset

Identifying outlier objects against main community evolution trends is not only meaningful itself for the purpose of finding novel evolution behaviors, but also helpful for better understanding the mainstream of community evolution. With the definition of community belongingness matrix of data objects, we constructed the transition matrix to least square optimize the pattern of evolutionary quantity between two consecutive belongingness snapshots. A set of properties about the transition matrix is discussed, which reveals its close relation to the step by step community membership change. The transition matrix is further optimized using robust regression methods by minimizing the disturbance incurred by the outliers, and the outlier factor of the anomalous object was defined. Being aware that large proportion of trivial but nomadic objects may exist in large datasets. This paper focus only on the influential community evolutionary outliers which both show remarkable difference from the main body of their community and sharp changes of their membership role within the communities. An algorithm on detection such kind of outliers are purposed in the paper. Experimental results on both synthetic and real world datasets show that the proposed approach is highly effective and efficient in discovering reasonable influential evolutionary community outliers.

Coalitional Planning in Game-like Domains via ATL Model Checking

Based on the planning via model checking paradigm, we address the problem of coalitional planning in this paper. Informally, coalitional planning is the problem of planning for a subset of agents in a multi-agent system to force the whole multi-agent system to satisfy some goals. We use the language of ATL as the goal language and the semantic structure of ATL, i.e., concurrent game structure, to formalize the planning domain. We separate the concept of goal and planning object and use execution structures to interpret the goals. And then, we define a algorithm for coalitional planning and formally prove its correctness. Distinguished from the previous work, in coalitional planning all the ATL formulas can be considered as goals, thus the expressive power of ATL is sufficiently applied.