Xin Huang

Bio: Xin Huang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Citation. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Topic-Sensitive Influential Paper Discovery in Citation Network

Abstract: Discovering important papers in different academic topics is known as topic-sensitive influential paper discovery. Previous works mainly find the influential papers based on the structure of citation networks but neglect the text information, while the text of documents gives a more precise description of topics. In our paper, we creatively combine both topics of text and the influence of topics over citation networks to discover influential articles. The observation on three standard citation networks shows that the existence of citations between papers is related to the topic of citing papers and the importance of cited papers. Based on this finding, we introduce two parameters to describe the topic distribution and the importance of a document. We then propose MTID, a scalable generative model, which generates the network with these two parameters. The experiment confirms superiority of MTID over other topic-based methods, in terms of at least 50% better citation prediction in recall, precision and mean reciprocal rank. In discovering influential articles in different topics, MTID not only identifies papers with high citations, but also succeeds in discovering other important papers, including papers about standard datasets and the rising stars.

Exponentially Twisted Sampling for Centrality Analysis and Community Detection in Attributed Networks

Abstract: In this paper, we conduct centrality analysis and community detection for attributed networks An attributed network, as a generalization of a graph, has node attributes and edge attributes that represent the “features” of nodes and edges Traditionally, centrality analysis and community detection of a graph are done by providing a sampling method, such as a random walk, for the graph To take node attributes and edge attributes into account, the sampling method in an attributed network needs to be twisted from the original sampling method in the underlining graph For this, we consider the family of exponentially twisted sampling methods and propose using path measures to specify how the sampling method should be twisted For signed networks, we define the influence centralities by using a path measure from opinions dynamics and the trust centralities by using a path measure from a chain of trust For attributed networks with node attributes, we also define advertisement-specific influence centralities by using a specific path measure that models influence cascades in such networks For networks with a distance measure, we define the path measure as the total distance along a path By specifying the desired average distance between two randomly sampled nodes, we are able to detect communities with various resolution parameters Various experiments are conducted to further illustrate these exponentially twisted sampling methods by using three real datasets: the political blogs, the MemeTracker dataset, and the WonderNetwork

Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

Abstract: Purpose: Relevance ranking algorithms rank retrieved documents based on the degrees of topical similarity (relevance) between search queries and documents. This paper aims to introduce a new relevance ranking method combining a probabilistic topic modeling algorithm with the “pennant retrieval” method using citation data. Data and Method: We applied this method to the iSearch corpus consisting of c. 435,000 physics papers. We first ran the topic modeling algorithm on titles and summaries of all papers for 65 search queries and obtained the relevance ranking lists. We then used the pennant retrieval to fuse the citation data with the existing relevance rankings, thereby incrementally refining the results. The outcome produced better relevance rankings with papers covering various aspects of the topic searched as well as the more marginal ones. The Maximal Marginal Relevance (MMR) algorithm was used to evaluate the retrieval performance of the proposed method by finding out its effect on relevance ranking algorithms that we used. Findings: Findings suggest that the terms used in different contexts in the papers might sometimes be overlooked by the topic modeling algorithm. Yet, the fusion of citation data to relevance ranking lists provides additional contextual information, thereby further enriching the results with diverse (interdisciplinary) papers of higher relevance. Moreover, results can easily be re-ranked and personalized. Implications: We argue that once it is tested on dynamic corpora for computational load, robustness, replicability, and scalability, the proposed method can in time be used in both local and international information systems such as TR-Dizin, Web of Science, and Scopus. Originality: The proposed method is, as far as we know, the first one that shows that relevance rankings produced with a topic modeling algorithm can be incrementally refined using pennant retrieval techniques based on citation data.