Social Network Analysis

Transactions of the Institute of British Geographers

Matrix Factorization Techniques for Recommender Systems

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources—the transmit precoding matrices of the MUs—and the computational resources—the CPU cycles/second assigned by the cloud to each MU—in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.

Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing

Passenger flow prediction is one of the most important issues in an urban subway system toward smart cities, which can help cut a trip time, plan a trip route, and thus provide a comfortable travel experience. However, there still exist some challenges on how to fully leverage effective and efficient passenger mobility patterns hidden in big traffic data to improve the accuracy of prediction. In this paper, we introduce a concept of the loyal passenger and propose a Spatio-Temporal Loyalty-based model (STLoyal) to improve the precision of prediction through analyzing the characteristics of loyal subway passengers. The proposed STLoyal model is evaluated using real-world subway transaction data sets in Shanghai, China, and it is compared with other state-of-the-art methods. The experimental results show that STLoyal yields superior prediction accuracy on weekdays and weekends in terms of loyalty, time, location, and weather metrics.

STLoyal: A Spatio-Temporal Loyalty-Based Model for Subway Passenger Flow Prediction

This paper considers the problem of controlling a complex dynamical network by means of pinning. We point out that the synchronization criterion given in the form of matrix eigenvalues can be equivalent to a linear matrix inequality criterion. We further investigate network synchronization via pinning and prove several linear matrix inequality theorems. In particular, we theoretically provide two typical pinning strategies based on whether the graph which is made up of unpinned nodes and edges between them is irreducible or not. Numerical simulations including k-regular networks, star-shaped networks and BA scale-free networks, are shown for illustration and verification.

https://iopscience.iop.org/article/10.1088/1751-8113/41/28/285102/pdf

LMI-based criteria for synchronization of complex dynamical networks

As a common human behavior, interaction is everywhere in human life. With the rise of big data and human–computer interaction in the 21st century, more and more researchers from different industries and disciplines pay great attention to the human interactive behavior research. From the perspective of computer science, scholars try to use computer technology to make research more meaningful. To the best of our knowledge, this paper is the first study to investigate the potential rules of human interactive behavior in the view of computer science, based on 16 top-tier journals of human interactive behavior from Microsoft Academic Graph dataset. We put forward a topic extraction and clustering model based on word2vec to infer key topics, which can be widely used in different fields of research. We find that the growth of human interactive behavior is in an uptrend on the whole. Besides, the cooperative relationship between authors and countries/regions is closer over time. We also make the mensurable evolution analysis of topics by a statistical method. Some topics are hot all the time, while some are unpopular as time goes by. Finally, we do rankings in the field of human behavior research from a new perspective. All these findings help researchers observe potential patterns and the topic evolution in half a century, which may shed dazzling light on the exploration of human interactive behavior.

/pdf/human-interactive-behavior-a-bibliographic-review-1rlvluw196.pdf

Human Interactive Behavior: A Bibliographic Review

It is laborious for researchers to find proper collaborators who can provide researching guidance besides collaborating. Beneficial Collaborators (BCs), researchers who have a high academic level and relevant topics, can genuinely help researchers to enrich their research. Though many efforts have made to develop collaborator recommendation, most of existing works have mainly focused on recommending most possible collaborators with no intention to recommend specifically the BCs. In this paper, we propose the Beneficial Collaborator Recommendation (BCR) model that considers the dynamic research interest of researcher's and academic level of collaborators to recommend the BCs. First, we run the LDA model on the abstract of researchers' publications in each year for topic clustering. Second, we fix generated topic distribution matrix by a time function to fit interest dynamic transformation. Third, we compute the similarity between the collaboration candidate's feature matrix and the target researcher. Finally, we combine the similarity and influence in collaborators network to fix rank score and mine the candidates with high academic level and academic social impact. BCR generates the topN BCs recommendation. Extensive experiments on a dataset with citation network demonstrate that BCR performs better in terms of precision, recall, F1 score and the recommendation quality compared to baseline methods.

Random Walk-based Beneficial Collaborators Recommendation Exploiting Dynamic Research Interests and Academic Influence

Measuring the impact of authors can not only be a good guidance for new researchers, but also provide a standard for academic foundations and awards. Heterogeneous networks can capture more information about the interactions between entities and they are more and more widely used for the measurement of author impact. However, most of the existing researches take all the papers into the networks as equal, although they have different importance levels. In this paper, we propose a new model: TAPRank, which calculates author impact in author-paper network with considering the PageRank scores of papers for the first time. The PageRank algorithm is implemented in paper citation network, taking the time of publication of each paper into consideration. In addition, the experiments on DBLP dataset show a better performance of TAPRank than other state-of-the-art models.

Xiangjie Kong

Papers

STLoyal: A Spatio-Temporal Loyalty-Based Model for Subway Passenger Flow Prediction

LMI-based criteria for synchronization of complex dynamical networks

Human Interactive Behavior: A Bibliographic Review

Random Walk-based Beneficial Collaborators Recommendation Exploiting Dynamic Research Interests and Academic Influence

TAPRank: A Time-Aware Author Ranking Method in Heterogeneous Networks