Collaboration Recommendation on Academic Social Networks

Lopes, Giseli Rabello; Moro, Mirella M.; Wives, Leandro Krug; Oliveira, José Palazzo Moreira de

doi:10.1007/978-3-642-16385-2_24

Cited by 63 publications

(30 citation statements)

References 10 publications

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“…Huynh et al proposed new methods for calculating similarity of vertices in the coauthor network by taking the trend information into considering relational strength of different authors [8]. In another study, Lopes et al proposed an innovative approach for recommending new collaborators and for intensifying existing collaborations [9]. They considered the semantic issues involved in the relationships between researchers in different areas, as well as structural issues, by analyzing existing relationships between researchers.…”

Section: Related Workmentioning

confidence: 98%

“…Predicting future coauthors is not a trivial task. Content-based and link-based methods are efficient for link prediction and most current research is focused on evaluating the performance of proposed methods based on the quantity or accuracy of coauthor link prediction [9][10]. However, collaborator recommendation is different from coauthor link prediction and current methods do not select the recommendation results based on the quality of collaborations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Collaborator Recommendation for Isolated Researchers

Huynh¹,

Takasu²,

Masada³

et al. 2014

2014 28th International Conference on Advanced Information Networking and Applications Workshops

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Successful research collaborations may facilitate major outcomes in science and their applications. Thus, identifying effective collaborators may be a key factor that affects success. However, it is very difficult to identify potential collaborators and it is particularly difficult for young researchers who have less knowledge about other researchers and experts in their research domain. This study introduces and defines the problem of collaborator recommendation for 'isolated' researchers who have no links with others in coauthor networks. Existing approaches such as link-based and contentbased methods may not be suitable for isolated researchers because of their lack of links and content information. Thus, we propose a new approach that uses additional information as new features to make recommendations, i.e., the strength of the relationship between organizations, the importance rating, and the activity scores of researchers. We also propose a new method for evaluating the quality of collaborator recommendations. We performed experiments by crawling publications from the Microsoft Academic Search website. The metadata were extracted from these publications, including the year, authors, organizational affiliations of authors, citations, and references. The metadata from publications between 2001 and 2005 were used as the training data while those from 2006 to 2011 were used for validation. The experimental results demonstrated the effectiveness and efficiency of our proposed approach.

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Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Collaborator Recommendation for Isolated Researchers

Huynh¹,

Takasu²,

Masada³

et al. 2014

2014 28th International Conference on Advanced Information Networking and Applications Workshops

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show abstract

“…In [62], [63], Phelan et al proposed a recommendation system Buzzer, which uses a content-based approach to rank RSS news by mining trending terms from public or private Twitter timelines. In [65], Lopes et al presented an approach to recommend collaborations on the context of academic social networks. Zhao et al [67] studied ranking algorithms for microblogs (namely Twitter) and their search algorithm takes six social network properties into consideration.…”

Section: Social Network Analysismentioning

confidence: 99%

A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects

Zhu

Shi

et al. 2013

IEEE Commun. Surv. Tutorials

269

150

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Delay tolerant networks (DTNs) may lack continuous network connectivity. Routing in DTNs is thus challenging since it must handle network partitioning, long delays, and dynamic topology in such networks. In recent years, social-based approaches, which attempt to exploit social behaviors of DTN nodes to make better routing decision, have drawn tremendous interests in DTN routing design. In this article, we summarize the social properties in DTNs, and provide a survey of recent social-based DTN routing approaches. To improve routing performance, these methods either take advantages of positive social characteristics such as community and friendship to assist packet forwarding or consider negative social characteristics such as selfishness. We conclude by discussing some open issues and challenges in socialbased approaches regarding the design of DTN routing protocols.

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“…Hầu hết các nghiên cứu gần đây của các nhóm này đều quan tâm đến hướng phân tích mạng xã hội (cụ thể là mạng đồng tác giả) [3,4]. Trong các nghiên cứu gần đây, thì tiếp cận phân tích mạng xã hội đã cho thấy đây là một hướng tiếp cận tiềm năng và bước đầu khá thành công trong việc phát triển các phương pháp khuyến nghị trong nghiên cứu khoa học [3,4,12,13,14,17,21]. Tuy nhiên, các nghiên cứu liên quan kể trên đều chưa quan tâm đến yếu tố xu hướng cộng tác khi phân tích các mối quan hệ trong mạng.…”

Section: Giới Thiệuunclassified

New methods for calculating trend-based vertex similarity for collaboration recommendation

Huynh

Hoang²

2014

JCC

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Tóm tắt. Khuyến nghị cộng tác trong nghiên cứu khoa học là bài toán tự động liệt kê những người, nhóm nghiên cứu cộng tác tiềm năng ứng với đầu vào là một người, nhóm nghiên cứu nào đó. Đây là bài toán được quan tâm bởi những chuyên gia trong lĩnh vực này trong thời gian gần đây. Tiếp cận phổ biến đã và đang được các nghiên cứu này áp dụng là dựa vào phân tích mạng xã hội, cụ thể là phân tích các mối quan hệ trong mạng đồng tác giả, cũng như ảnh hưởng của nó đối với việc tìm ra những người, nhóm nghiên cứu cộng tác tiềm năng. Tuy nhiên, các phương pháp hiện nay đều chưa quan tâm đến thông tin về xu hướng cộng tác, một yếu tố quan trọng trong việc hình thành các mối quan hệ cộng tác mới. Bài báo này đề xuất ba phương pháp mới để tính tương tự đỉnh trong mạng đồng tác giả: (1) Maximum Path based Relation Strength (M P RS); (2) Maximum Path based Relation Strength Plus (M P RS+); và (3) Relation Strength Similarity Plus (RSS+). Hai phương pháp M P RS+ và RSS+ có sử dụng thông tin về xu hướng cộng tác để cải tiến việc tính toán mức độ quan hệ của những người nghiên cứu trong mạng đồng tác giả. Các phương pháp đề xuất ứng dụng vào bài toán khuyến nghị cộng tác ở mức cá nhân người nghiên cứu. Thực nghiệm được tiến hành trên hai tập dữ liệu khoa học: i) cơ sở dữ liệu khoa học mở 'Digital Bibliography & Library Project' (DBLP); ii) tập dữ liệu do chúng tôi rút trích từ trang web của hệ thống Microsoft Academic Search (gọi tắt là MAS). Kết quả thực nghiệm cho thấy các phương pháp đề xuất cho kết quả tốt hơn các phương pháp tương tự đỉnh truyền thống và phổ biến hiện nay.Từ khóa. Hệ khuyến nghị (recommender system), cộng tác nghiên cứu (research collaboration), khuyến nghị cộng tác (collaboration recommendation), phân tích mạng đồng tác giả (co-author network analysis), xu hướng cộng tác (collaborative trend).Abstract. Collaboration recommendation is a problem that automatically selects and provides a list of potential researchers or research groups with respect to the input which is a researcher or a research group. Recently, this problem has attracted a lot of attention of many researchers in this area. A popular approach for collaboration recommendation problem is based on social network analysis, specifically co-author network analysis. However, the current methods do not consider collaborative trend in analyzing co-author network and the collaborative trend is one of the key factors for forming new co-authorships. In this paper, we propose three new methods: (1) Maximum Path based Relation Strength (MPRS); (2) Maximum Path based Relation Strength Plus (MPRS+); and (3) Relation Strength Similarity Plus (RSS+), for modeling and calculating vertex similarity in the co-author network. In our trend-based methods (MPRS+, RSS+), information of collaborative trend is used to improve the calculation of relation strength for researchers in the co-author network. The proposed methods are applied for researcher collaboration recommendation. Experiments are conducted on two dataset: i) Digital Bibliography & Library Project (DBLP...

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Collaboration Recommendation on Academic Social Networks

Cited by 63 publications

References 10 publications

Collaborator Recommendation for Isolated Researchers

Collaborator Recommendation for Isolated Researchers

A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects

New methods for calculating trend-based vertex similarity for collaboration recommendation

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