Using link semantics to recommend collaborations in academic social networks

Brandão, Michele A.; Moro, Mirella M.; Lopes, Giseli Rabello; Oliveira, José Palazzo M. de

doi:10.1145/2487788.2488058

Cited by 41 publications

(34 citation statements)

References 18 publications

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“…Regardless of its significative results, this study extrapolates the topological information of the network; it relies on information that, often, is not available or is not welldefined. This same limitation is faced by Brandao et al [4] and Lim et al [16].…”

Section: Related Workmentioning

confidence: 79%

Multimodal graph-based analysis over the DBLP repository

Gimenes

Gualdron

Rodrigues

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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The use of graph theory for analyzing network-like data has gained central importance with the rise of the Web 2.0. However, many graph-based techniques are not welldisseminated and neither explored at their full potential, what might depend on a complimentary approach achieved with the combination of multiple techniques. This paper describes the systematic use of graph-based techniques of different types (multimodal) combining the resultant analytical insights around a common domain, the Digital Bibliography & Library Project (DBLP). To do so, we introduce an analytical ensemble based on statistical (degree, and weakly-connected components distribution), topological (average clustering coefficient, and effective diameter evolution), algorithmic (link prediction/machine learning), and algebraic techniques to inspect non-evident features of DBLP at the same time that we interpret the heterogeneous discoveries found along the work. As a result, we have put together a set of techniques demonstrating over DBLP what we call multimodal analysis, an innovative process of information understanding that demands a wide technical knowledge and a deep understanding of the data domain. We expect that our methodology and our findings will foster other multimodal analyses and also that they will bring light over the Computer Science research.

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Section: Related Workmentioning

confidence: 79%

Multimodal graph-based analysis over the DBLP repository

Gimenes

Gualdron

Rodrigues

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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show abstract

“…Therefore, the trust between the target user and all the ignored users would be the same; often, it is 0. This, in turn, would decrease the usefulness of the trust matrix in the final prediction calculation in Equation (6). If the trust value is 0, Equation (6) simply results in a mean rating.…”

Section: Sparsity Of the Weight Matrixmentioning

confidence: 99%

AgreeRelTrust—a Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems

2019

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Recommendation systems alleviate the problem of information overload by helping users find information relevant to their preference. Memory-based recommender systems use correlation-based similarity to measure the common interest among users. The trust between users is often used to address the issues associated with correlation-based similarity measures. However, in most applications, the trust relationships between users are not available. A popular method to extract the implicit trust relationship between users employs prediction accuracy. This method has several problems such as high computational cost and data sparsity. In this paper, addressing the problems associated with prediction accuracy-based trust extraction methods, we proposed a novel trust-based method called AgreeRelTrust. Unlike accuracy-based methods, this method does not require the calculation of initial prediction and the trust relationship is more meaningful. The collective agreements between any two users and their relative activities are fused to obtain the trust relationship. To evaluate the usefulness of our method, we applied it to three public data sets and compared the prediction accuracy with well-known collaborative filtering methods. The experimental results show our method has large improvements over the other methods.

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“…1 is that coAfrequency is a nonnormalized metric, i.e., the set of weights of the datasets is not in the range 0 to 1. In order to solve this problem, we try to normalize coAfrequency by using two methods: the norm (equal to the Euclidean distance) of the set of weights that can be seen as a vector [2] and the unitybased normalization 5 . However, the first method is not appropriate, because the norm of the coAfrequency vector is very high, which reduces most of the weights to the magnitude of 10 4 .…”

Section: Tieness: a New Metric For The Strength Of Tiesmentioning

confidence: 99%

“…Then, properties and features can be extracted from the graph, and metrics can be applied to nodes and edges in order to better understand the individuals' social behavior. Finally, there are many interesting applications based on such networks, including (but definitely not limited to) ranking individuals and their groups, link prediction, information diffusion, recommendation, and pattern analysis (e.g., [5,14,22]). …”

Section: Introductionmentioning

confidence: 99%

“…Specifically, studying the strength of co-authorship ties may reveal how its behaviors relate to research and how any application based on co-authorship patterns may benefit. For instance, new strength-related metrics could help existing works on measuring research productivity [12] and ranking researchers [14] and their graduate programs [21], as well as recommending collaborations [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The strength of co-authorship ties through different topological properties

Brandão

Moro

2017

J Braz Comput Soc

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Social networks are complex structures that describe individuals (graph nodes) connected in any social context (graph edges). Different metrics can be applied to those networks and their properties in order to understand behavior and even predict the future. One of such properties is tie strength, which allows to identify prominent individuals, analyze how relationships play different roles, predict links, and so on. Here, we specifically address the problem of measuring tie strength in co-authorship social networks (nodes are researchers and edges represent their co-authored publications). We start by presenting four cases that emphasize the problems of current metrics. Then, we propose a new metric for tie strength, called tieness, that is simple to calculate and better differentiates the degrees of strength. Accompanied with a nominal scale, tieness also provides better results when compared to the existing metrics. Our analyses consider three real social networks built from publications collected from digital libraries on Computer Science, Medicine, and Physics. Finally, we also make all datasets publicly available.

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Using link semantics to recommend collaborations in academic social networks

Cited by 41 publications

References 18 publications

Multimodal graph-based analysis over the DBLP repository

Multimodal graph-based analysis over the DBLP repository

AgreeRelTrust—a Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems

The strength of co-authorship ties through different topological properties

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