Potential collaboration discovery using document clustering and community structure detection

Santos, Cristian Klen dos; Evsukoff, Alexandre G.; Lima, Beatriz Souza Leite Pires de; Ebecken, Nelson F. F.

doi:10.1145/1651274.1651283

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…The only limitation of LC-method is that it requires the graph clustering algorithm to have a step where nodes are assigned to intermediate clusters. Spectral clustering is another such algorithm, but in our experiments (results not reported here), we found that the modularity optimization algorithm performed considerably better than spectral clustering, which is in agreement with what is reported in [4]. Another contribution of this work is to show that the relationship in Equation 3 is also observed in the one-mode projection arising from document-word bipartite graphs.…”

Section: Discussionsupporting

confidence: 91%

“…While community detection algorithms have been extensively used by physicists specially for social network analysis [3,5,13], to the best of our knowledge the application of these methods for document clustering has not been studied much in the literature, except for the works by dos Santos et al [4] and by Pivovarov and Trunov [14]. In this paper, we propose an approach to document clustering by applying a modularity optimization algorithm based on [12] to the graph of documents that is obtained by one-mode projection of a document-word bipartite graph.…”

Section: Introductionmentioning

confidence: 99%

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Text clustering using one-mode projection of document-word bipartite graphs

Srivastava

Soto

Milios

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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Many real life networks have an underlying bipartite structure based on which similarity between two nodes or data instances can be defined. For example, in the case of a document corpus, the similarity between a pair of documents can be assumed to arise from the words that co-occur in them, and this document-word co-occurrence relationship can be modeled as a bipartite graph. A document similarity graph can be obtained by taking a one-mode projection of the bipartite graph, which is a popular technique for studying similar networks which arise from bipartite structure. A graph-based clustering algorithm can then be applied to this projection graph to obtain clusters of documents. In this paper we study the use of one-mode projection of the document-word bipartite graph and the subsequent application of a modularity optimization algorithm to cluster the documents. In particular, we propose an alternative and faster algorithm, which works in two-steps: first, finding the documents that are easy to cluster, and then, assigning the remaining documents to the existing or new clusters. We show that the algorithms based on one-mode projections perform significantly better than traditional clustering approaches. In addition, our method has similar or better clustering performance than the most popular algorithm for modularity optimization, while also running four times faster.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Text clustering using one-mode projection of document-word bipartite graphs

Srivastava

Soto

Milios

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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“…The clustering community detection method could discover the potential cooperative rela-tionship of researchers (Santos et al, 2009). From the perspective of citation, a potential cooperation possibility measurement model for researchers in two different research fields was constructed to demonstrate the feasibility of using direct citation to tap potential cooperators (Ma, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of potential interdisciplinary cooperative topics based on co-word network: Taking LIS and computer science as an example

2022

DSI

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Purpose/significance] Interdisciplinary knowledge fusion plays a key role in promoting the development of interdisciplinary integration and providing new ideas for interdisciplinary cooperative research. This study sets out to identify potential interdisciplinary cooperative topics between Library and Information Science (LIS) and Computer Science. [Method / Process] We built an interdisciplinary co-word network to identify potential interdisciplinary cooperative topics by closed and opened irrelevant knowledge discovery methods. We also constructed the topic interdisciplinary cooperation potential index (TICPI) to calculate the interdisciplinary cooperation potential of the topic and found the best contact path of the cooperation topic by constructing the practicable value (PV) of the contact patch. [Result / Conclusion] The experimental data suggested that both methods can identify the same potential interdisciplinary cooperative topics, such as knowledge service & matrix decomposition, online comments & social media processing, academic text & generative adversarial network, network public opinion & smart home. Exploiting the cooperation potential of these topics can help the knowledge fusion between disciplines.

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Scholarly recommendation systems: a literature survey

Zhang,

Patra,

Yaseen

et al. 2023

Knowl Inf Syst

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A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users’ feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain.

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Potential collaboration discovery using document clustering and community structure detection

Cited by 6 publications

References 15 publications

Text clustering using one-mode projection of document-word bipartite graphs

Text clustering using one-mode projection of document-word bipartite graphs

Identification of potential interdisciplinary cooperative topics based on co-word network: Taking LIS and computer science as an example

Scholarly recommendation systems: a literature survey

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