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DINE : a framework for deep incomplete network embedding

- Hou, Ke, Liu, Jiaying, Peng, Yin, Xu, Bo, Lee, Ivan, Xia, Feng

  • Authors: Hou, Ke , Liu, Jiaying , Peng, Yin , Xu, Bo , Lee, Ivan , Xia, Feng
  • Date: 2019
  • Type: Text , Conference paper
  • Relation: 32nd Australasian Joint Conference on Artificial Intelligence, AI 2019 Vol. 11919 LNAI, p. 165-176
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  • Description: Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structures or node attributes. While embedding techniques on complete networks have been intensively studied, in real-world applications, it is still a challenging task to collect complete networks. To bridge the gap, in this paper, we propose a Deep Incomplete Network Embedding method, namely DINE. Specifically, we first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. To improve the embedding performance, we consider both network structures and node attributes to learn node representations. Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks. The results demonstrate the superiority of our proposed approach compared against state-of-the-art baselines. © 2019, Springer Nature Switzerland AG.
  • Description: E1

Quantifying success in science : an overview

- Bai, Xiaomei, Pan, Habxiao, Hou, Jie, Guo, Teng, Lee, Ivan, Xia, Feng

  • Authors: Bai, Xiaomei , Pan, Habxiao , Hou, Jie , Guo, Teng , Lee, Ivan , Xia, Feng
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 123200-123214
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  • Description: Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions. © 2013 IEEE.
  • Description: This work was supported in part by the Liaoning Provincial Key Research and Development Guidance Project under Grant 2018104021, and in part by the Liaoning Provincial Natural Fund Guidance Plan under Grant 20180550011.

Detecting outlier patterns with query-based artificially generated searching conditions

- Yu, Shuo, Xia, Feng, Sun, Yuchen, Tang, Tao, Yan, Xiaoran, Lee, Ivan

  • Authors: Yu, Shuo , Xia, Feng , Sun, Yuchen , Tang, Tao , Yan, Xiaoran , Lee, Ivan
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 1 (2021), p. 134-147
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  • Description: In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE.

API : an index for quantifying a scholar's academic potential

- Ren, Jing, Wang, Lei, Wang, Kailai, Yu, Shuo, Hou, Mingliang, Lee, Ivan, Kong, Xiangje, Xia, Feng

  • Authors: Ren, Jing , Wang, Lei , Wang, Kailai , Yu, Shuo , Hou, Mingliang , Lee, Ivan , Kong, Xiangje , Xia, Feng
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 7, no. (2019), p. 178675-178684
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  • Description: In the context of big scholarly data, various metrics and indicators have been widely applied to evaluate the impact of scholars from different perspectives, such as publication counts, citations, ${h}$-index, and their variants. However, these indicators have limited capacity in characterizing prospective impacts or achievements of scholars. To solve this problem, we propose the Academic Potential Index (API) to quantify scholar's academic potential. Furthermore, an algorithm is devised to calculate the value of API. It should be noted that API is a dynamic index throughout scholar's academic career. By applying API to rank scholars, we can identify scholars who show their academic potentials during the early academic careers. With extensive experiments conducted based on the Microsoft Academic Graph dataset, it can be found that the proposed index evaluates scholars' academic potentials effectively and captures the variation tendency of their academic impacts. Besides, we also apply this index to identify rising stars in academia. Experimental results show that the proposed API can achieve superior performance in identifying potential scholars compared with three baseline methods. © 2019 IEEE.

OFFER: A Motif Dimensional Framework for Network Representation Learning

- Yu, Shuo, Xia, Feng, Xu, Jin, Chen, Zhikui, Lee, Ivan

  • Authors: Yu, Shuo , Xia, Feng , Xu, Jin , Chen, Zhikui , Lee, Ivan
  • Date: 2020
  • Type: Text , Conference proceedings
  • Relation: 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 p. 3349-3352
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  • Description: Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency. © 2020 ACM.

Web of scholars : a scholar knowledge graph

- Liu, Jiaying, Ren, Jing, Zheng, Wenqing, Chi, Lianhua, Lee, Ivan, Xia, Feng

  • Authors: Liu, Jiaying , Ren, Jing , Zheng, Wenqing , Chi, Lianhua , Lee, Ivan , Xia, Feng
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020 p. 2153-2156
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  • Description: In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provides services for fast, accurate, and intelligent semantic querying as well as powerful recommendations. In addition, in order to realize information sharing, it provides open API to be served as the underlying architecture for advanced functions. Web of Scholars takes advantage of knowledge graph, which means that it will be able to access more knowledge if more search exist. It can be served as a useful and interoperable tool for scholars to conduct in-depth analysis within Science of Science. © 2020 ACM.

An Observation of research complexity in top universities based on research publications

- Lee, Ivan, Xia, Feng, Roos, Goran

  • Authors: Lee, Ivan , Xia, Feng , Roos, Goran
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: WWW '17 Companion; Perth, Australia; 3-7 April, 2017 ; Published in Proceedings of the 26th International Conference on World Wide Web Companion April 2017 p. 1259-1265
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  • Description: This paper investigates research specialisation of top ranked universities around the world. The revealed comparative advantage in different research fields are determined according to the number of research articles published. Subsequently, measures of research ubiquity and diversity, and research complexity index of each university, are obtained and discussed. The study is conducted on top-ranked universities according to Shanghai Jiao Tong Academic Ranking of World Universities, with bibliographical details extracted Microsoft Academic Graph data set and research fields of journals labelled with SCImago Journal Classification. Diversity-ubiquity distributions, relevance of RCI and university ranks, and geographical RCI distributions are examined in this paper.

On the correlation between research complexity and academic competitiveness

- Ren, Jing, Lee, Ivan, Wang, Lei, Chen, Xiangtai, Xia, Feng

  • Authors: Ren, Jing , Lee, Ivan , Wang, Lei , Chen, Xiangtai , Xia, Feng
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Kyoto, Japan, 30 November to 1 December 2020, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12504 LNCS, p. 416-422
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  • Description: Academic capacity is a common way to reflect the educational level of a country or district. The aim of this study is to explore the difference between the scientific research level of institutions and countries. By proposing an indicator named Citation-weighted Research Complexity Index (CRCI), we profile the academic capacity of universities and countries with respect to research complexity. The relationships between CRCI of universities and other relevant academic evaluation indicators are examined. To explore the correlation between academic capacity and economic level, the relationship between research complexity and GDP per capita is analysed. With experiments on the Microsoft Academic Graph data set, we investigate publications across 183 countries and universities from the Academic Ranking of World Universities in 19 research fields. Experimental results reveal that universities with higher research complexity have higher fitness. In addition, for developed countries, the development of economics has a positive correlation with scientific research. Furthermore, we visualize the current level of scientific research across all disciplines from a global perspective. © 2020, Springer Nature Switzerland AG.
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