Untitled

Zhang, Jin; Luo, An; Tang, Tao; Hong, Yi

doi:10.1002/asi.v60:10

Cited by 42 publications

(5 citation statements)

References 509 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As seen in Fig. 1, the co-authorship network's characteristics are similar to that of a typical scale-free network, where a few nodes (authors) have high number of connections and most nodes have low number of connections (Yan and Ding 2009). The average out-degree of the network is 4.86 and the maximum out-degree is 152.…”

Section: Dataset and Experiments Setupmentioning

confidence: 56%

On the optimal solution of budgeted influence maximization problem in social networks

Güney

2017

Oper Res Int J

View full text Add to dashboard Cite

The budgeted influence maximization problem is a challenging stochastic optimization problem defined on social networks. In this problem, the objective is identifying influential individuals who can influence the maximum number of members within a limited budget. In this work an integer program that approximates the original problem is developed and solved by a sample average approximation (SAA) scheme. Experimental analyses indicate that SAA method provides better results than the greedy method without worsening the solution time performance.

show abstract

Section: Dataset and Experiments Setupmentioning

confidence: 56%

On the optimal solution of budgeted influence maximization problem in social networks

Güney

2017

Oper Res Int J

View full text Add to dashboard Cite

show abstract

“…After this attempt, various studies applied social network analysis techniques on co-authorship networks, mainly focused on the centrality of scholars. Erjia and Ying (2009) validated that centrality of scholars and the number of their citations are significantly related. In their study, betweenness centrality and the number of citations showed the highest correlation.…”

Section: Network-based Indicatorsmentioning

confidence: 85%

Is Performance of Scholars Correlated to Their Research Collaboration Patterns?

2019

View full text Add to dashboard Cite

This study aims to validate whether the research performance of scholars correlates with how the scholars work together. Although the most straightforward approaches are centrality measurements or community detection, scholars mostly participate in multiple research groups and have different roles in each group. Thus, we concentrate on the subgraphs of co-authorship networks rooted in each scholar that cover (i) overlapping of the research groups on the scholar and (ii) roles of the scholar in the groups. This study calls the subgraphs "collaboration patterns" and applies subgraph embedding methods to discover and represent the collaboration patterns. Based on embedding the collaboration patterns, we have clustered scholars according to their collaboration styles. Then, we have examined whether scholars in each cluster have similar research performance, using the quantitative indicators. The coherence of the indicators cannot be solid proofs for validating the correlation between collaboration and performance. Nevertheless, the examination for clusters has exhibited that the collaboration patterns can reflect research styles of scholars. This information will enable us to predict the research performance more accurately since the research styles are more consistent and sustainable features of scholars than a few high-impact publications.

show abstract

“…Centrality theory [8][9][10][11][12], diffusion models [13], heat diffusion theory [14], evidence theory [15] etc., are the most frequently used techniques for obtaining top-K influential nodes in a network.…”

Section: Related Workmentioning

confidence: 99%

“…Many papers have been written to show co-relations among various centrality measures and to date, research has been carried out to answers these questions: which centrality measure is best for obtaining the top-K influential nodes in a given network, which centrality measure is the best fit for a given type of data etc. Yan et al [8] studied the correlation between degree centrality, closeness centrality, betweenness centrality and PageRank centrality in a co authorship network. Benzi et al [9] studied the correlation between sub-graph centrality and total communicability.…”

Section: Katz Centralitymentioning

confidence: 99%