Jiaxing Shang

How Overlapping Community Structure Affects Epidemic Spreading in Complex Networks

Many real-world networks exhibit overlapping community structure in which vertices may belong to more than one community. It has been recently shown that community structure plays an import role in epidemic spreading. However, the effect of different vertices on epidemic behavior was still unclear. In this paper, we classify vertices into overlapping and non-overlapping ones, and investigate in detail how they affect epidemic spreading respectively. We propose a SIR epidemic model named ICP-SIR (Inner-Community Preferred Susceptible-Infective-Recovered) where the inner-community and inter-community spreading rates are different. We consider the case where epidemic process is started by immunizing and infecting multiple overlapping or non-overlapping vertices. The epidemic model is applied on both synthetic and real-world networks. Simulation results indicate that compared to non-overlapping vertices, overlapping vertices play a vital role in spreading the epidemic across communities. The result of our research may provide some reference on epidemic immunization in the future.

WSCN: Web Service Composition Based on Complex Networks

Currently the research of service science mainly focuses on the composition of distributed and heterogeneous Web services. Most of the approaches proposed to tackle this problem are based on cross-platform workflow and AI planning. However, until very recently, little attention is paid to the approaches based on complex networks. In the era of Big Data, Web services are created and updated in a fast way and their relationships are becoming more complicated. Therefore, studying the basic characteristics of services networks based on complex network theory will be meaningful. In this paper, we propose a framework to model the services network as a directed complex network, in which nodes represent the atomic Web services and the data communicated between them while edges represent the dependent relationship between data and service nodes. Based on the modeled network, we further apply graph search algorithms to solve the problem of Web services composition. Experimental results are given to validate the flexibility and efficiency of our framework.

Effective Influence Maximization Based on the Combination of Multiple Selectors

Influence maximization is an extensively studied optimization problem aiming at finding the best k seed nodes in a network such that they can influence the maximum number of individuals. Traditional heuristic or shortest path based methods either cannot provide any performance guarantee or require huge amount of memory usage, making themselves ineffective in real world applications. In this paper, we propose MSIM: a multi-selector framework which combines the intelligence of different existing algorithms. Our framework consists of three layers: (i) the selector layer; (ii) the combiner layer, and (iii) the evaluator layer. The first layer contains different selectors and each selector can be arbitrary existing influence maximization algorithm. The second layer contains several combiners and combines the output of the first layer in different ways. The third layer evaluates the candidates elected by the second layer to find the best seed nodes in an iterative manner. Experimental results on five real world datasets show that our framework always effectively finds better seed nodes than other state-of-the-art algorithms. Our work provides a new perspective to the study of influence maximization.

A Linear Time Algorithm for Influence Maximization in Large-Scale Social Networks

Influence maximization is the problem of finding k seed nodes in a given network as information sources so that the influence cascade can be maximized. To solve this problem both efficiently and effectively, in this paper we propose LAIM: a linear time algorithm for influence maximization in large-scale social networks. Our LAIM algorithm consists of two parts: (1) influence computation; and (2) seed nodes selection. The first part approximates the influence of any node using its local influence, which can be efficiently computed with an iterative algorithm. The second part selects seed nodes in a greedy manner based on the results of the first part. We theoretically prove that the time and space complexities of our algorithm are proportional to the network size. Experimental results on six real-world datasets show that our approach significantly outperforms other state-of-the-art algorithms in terms of influence spread, running time and memory usage.

App Uninstalls Prediction: A Machine Learning and Time Series Mining Approach

Nowadays mobile applications (a.k.a. app) are playing unprecedented important roles in our daily life and their research has attracted many scholars. However, traditional research mainly focuses on mining app usage patterns or making app recommendations, little attention is paid to the study of app uninstall behaviors. In this paper, we study the problem of app uninstalls prediction based on a machine learning and time series mining approach. Our approach consists of two steps: (1) feature construction and (2) model training. In the first step we extract features from the dynamic app usage data with a time series mining algorithm. In the second step we train classifiers with the extracted features and use them to predict whether a user will uninstall an app in the near future. We conduct experiments on the data collected from AppChina, a leading Android app marketplace in China. Results show that the features mined from time series data can significantly improve the prediction performance.