Xianghua Li

PR-Index: using the h-Index and PageRank for determining true impact

Several technical indicators have been proposed to assess the impact of authors and institutions. Here, we combine the h-index and the PageRank algorithm to do away with some of the individual limitations of these two indices. Most importantly, we aim to take into account value differences between citations-evaluating the citation sources by defining the h-index using the PageRank score rather than with citations. The resulting PR-index is then constructed by evaluating source popularity as well as the source publication authority. Extensive tests on available collections data (i.e., Microsoft Academic Search and benchmarks on the SIGKDD innovation award) show that the PR-index provides a more balanced impact measure than many existing indices. Due to its simplicity and similarity to the popular h-index, the PR-index may thus become a welcome addition to the technical indices already in use. Moreover, growth dynamics prior to the SIGKDD innovation award indicate that the PR-index might have notable predictive power.

Combination methods for identifying influential nodes in networks

Identifying influential nodes is of theoretical significance in many domains. Although lots of methods have been proposed to solve this problem, their evaluations are under single-source attack in scale-free networks. Meanwhile, some researches have speculated that the combinations of some methods may achieve more optimal results. In order to evaluate this speculation and design a universal strategy suitable for different types of networks under the consideration of multi-source attacks, this paper proposes an attribute fusion method with two independent strategies to reveal the correlation of existing ranking methods and indicators. One is based on feature union (FU) and the other is based on feature ranking (FR). Two different propagation models in the fields of recommendation system and network immunization are used to simulate the efficiency of our proposed method. Experimental results show that our method can enlarge information spreading and restrain virus propagation in the application of recommendation system and network immunization in different types of networks under the condition of multi-source attacks.

A Hybrid Algorithm for Estimating Origin-Destination Flows

With the development of intelligent transportation systems, the estimation of traffic flow in urban areas has attracted a great attention of researchers. The timely and accurate travel information of urban residents could assist users in planning their travel strategies and improve the operational efficiency of intelligent transportation systems. Currently, the origin-destination (OD) flows of urban residents are formulated as an OD matrix, which is used to denote the travel patterns of urban residents. In this paper, a simple and effective model, called NMF-AR, is proposed for predicting the OD matrices through combining the nonnegative matrix factorization (NMF) algorithm and the Autoregressive (AR) model. The basic characteristics of travel flows are first revealed based on the NMF algorithm. Then, the nonlinear time series coefficient matrix, extracted from the NMF algorithm, is estimated based on the AR model. Finally, we predict OD matrices based on the estimated coefficient matrix and the basis matrix of NMF. Extensive experiments have been implemented, in collected real data about taxi GPS information in Beijing, for comparing our proposed algorithm with some known methods, such as different kinds of

A Physarum-inspired optimization algorithm for load-shedding problem

K

Network Community Detection based on the Physarum-inspired Computational Framework

-nearest neighbor algorithms, neural network algorithms and classification algorithms. The results show that our proposed NMF-AR algorithm have a more effective capability in predicting OD matrices than other models.

A Physarum -Inspired Ant Colony Optimization for Community Mining

Abstract Load-shedding is an intentional reduction approach which can maintain the stability of a microgrid system effectively. Recent studies have shown that a load-shedding problem can be solved by formulating it as a 0/1 knapsack problem (KP). Although approximate solutions of 0/1 KP can be given by ant colony optimization (ACO) algorithms, adopting them requests a delicate consideration of the robustness, convergence rate and premature convergence. This paper proposes a new kind of Physarum-based hybrid optimization algorithm, denoted as PM-ACO, based on the critical paths reserved feature of Physarum-inspired mathematical (PM) model. Through adding additional pheromone to those important items selected by the PM model, PM-ACO improves the selection probability of important items and emerge a positive feedback process to generate optimal solutions. Comparing with other 0/1 KP solving algorithms, our experimental results demonstrate that PM-ACO algorithms have a stronger robustness and a higher convergence rate. Moreover, PM-ACO provides adaptable solutions for the load-shedding problem in a microgrid system.

A bio-inspired genetic algorithm for community mining

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, a kind of slime, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost.

A novel source locating strategy without consistent assumptions

Community mining is a powerful tool for discovering the knowledge of networks and has a wide application. The modularity is one of very popular measurements for evaluating the efficiency of community divisions. However, the modularity maximization is a NP-complete problem. As an effective optimization algorithm for solving NP-complete problems, ant colony based community detection algorithm has been proposed to deal with such task. However the low accuracy and premature still limit its performance. Aiming to overcome those shortcomings, this paper proposes a novel nature-inspired optimization for the community mining based on the Physarum, a kind of slime molds cells. In the proposed strategy, the Physarum-inspired model optimizes the heuristic factor of ant colony algorithm by endowing edges with weights. With the information of weights provided by the Physarum-inspired model, the optimized heuristic factor can improve the searching abilities of ant colony algorithms. Four real-world networks and two typical kinds of ant colony optimization algorithms are used for estimating the efficiency of proposed strategy. Experiments show that the optimized ant colony optimization algorithms can achieve a better performance in terms of robustness and accuracy with a lower computational cost.

Nature-Inspired Computational Model for Solving Bi-objective Traveling Salesman Problems.

The community structure as a vital property for complex networks contributes a lot for understanding and detecting inherent functions of real networks. However, existing algorithms which are ranging from the optimization-based to model-based strategies still need to be strengthened further in terms of their robustness and accuracy. In this paper, a kind of multi-headed slime molds, Physarum, is used for optimizing genetic algorithm (GA), due to its intelligence of generating foraging networks based on bioresearches. Thus, a Physarum-based Network Model (PNM) is proposed based on the Physarum-based Model, which shows an ability of recognizing inter-community edges. Combining PNM with a genetic algorithm, a novel genetic algorithm, called PNGACD, is putting forward to enhance the GAs efficiency, in which a priori edge recognition of PNM is integrated into the phase of initialization. Moreover, experiments in six real-world networks are used to evaluate the efficiency of the proposed method. Results show that there is a remarkable improvement in term of the robustness and accuracy, which demonstrates that PNGACD has a better performance, compared with the existing algorithms.

Traffic Flow Fluctuation Analysis Based on Beijing Taxi GPS Data.

Locating the source of propagation is a ubiquitous but challenging problem in the field of complex networks. The traditional source location methods based on a set of observers can achieve a high locating accuracy. However, such high accuracy is based on the consistent assumption which means the propagation delays consistently follow a certain distribution in both the infected time calculation process and the source location process. Based on our simulation results and existing researches, we find that the real propagation delays, in some real-world scenarios, often break such consistent assumption and the predication accuracy of existing methods decline significantly in these circumstances. Therefore it raises a critical question: can we locate the infection source without assuming the distribution of propagation delays? In this paper, we first formulate the problem of locating source as inferring the parameters of propagation delays based on a set of observers. Then, we propose a novel reverse propagation strategy to locate infection source. Finally, a comprehensive comparisons are used to provide a quantitative analyses of our method. The results show that our strategy has a higher accuracy than the traditional methods without the consistent assumptions.