References of "Esmaeilzadeh Dilmaghani, Saharnaz 50030401"
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See detailCommunity Detection in Complex Networks: A Survey on Local Approaches
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

Scientific Conference (2021)

Early approaches of community detection algorithms often depend on the network’s global structure with a time complexity correlated to the network size. Local algorithms emerged as a more efficient ... [more ▼]

Early approaches of community detection algorithms often depend on the network’s global structure with a time complexity correlated to the network size. Local algorithms emerged as a more efficient solution to deal with large-scale networks with millions to billions of nodes. This methodology has shifted the attention from global structure towards the local level to deal with a network using only a portion of nodes. Investigating the state-of-the-art, we notice the absence of a standard definition of locality between community detection algorithms. Different goals have been explored under the local terminology of community detection approaches that can be misunderstood. This paper probes existing contributions to extract the scopes where an algorithm performs locally. Our purpose is to interpret the concept of locality in community detection algorithms. We propose a locality exploration scheme to investigate the concept of locality at each stage of an existing community detection workflow. We summarized terminologies concerning the locality in the state-of-the-art community detection approaches. In some cases, we observe how different terms are used for the same concept. We demonstrate the applicability of our algorithm by providing a review of some algorithms using our proposed scheme. Our review highlights a research gap in community detection algorithms and initiates new research topics in this domain. [less ▲]

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See detailInnovation Networks from Inter-organizational Research Collaborations
Esmaeilzadeh Dilmaghani, Saharnaz UL; Piyatumrong, Apivadee UL; Danoy, Grégoire UL et al

in Heuristics for Optimization and Learning (2020)

We consider the problem of automatizing network generation from inter-organizational research collaboration data. The resulting networks promise to obtain crucial advanced insights. In this paper, we ... [more ▼]

We consider the problem of automatizing network generation from inter-organizational research collaboration data. The resulting networks promise to obtain crucial advanced insights. In this paper, we propose a method to convert relational data to a set of networks using a single parameter, called Linkage Threshold (LT). To analyze the impact of the LT-value, we apply standard network metrics such as network density and centrality measures on each network produced. The feasibility and impact of our approach are demonstrated by using a real-world collaboration data set from an established research institution. We show how the produced network layers can reveal insights and patterns by presenting a correlation matrix. [less ▲]

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See detailLocal Community Detection Algorithm with Self-defining Source Nodes
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Complex Networks & Their Applications IX (2020, September 01)

Surprising insights in community structures of complex networks have raised tremendous interest in developing various kinds of community detection algorithms. Considering the growing size of existing ... [more ▼]

Surprising insights in community structures of complex networks have raised tremendous interest in developing various kinds of community detection algorithms. Considering the growing size of existing networks, local community detection methods have gained attention in contrast to global methods that impose a top-down view of global network information. Current local community detection algorithms are mainly aimed to discover local communities around a given node. Besides, their performance is influenced by the quality of the source node. In this paper, we propose a community detection algorithm that outputs all the communities of a network benefiting from a set of local principles and a self-defining source node selection. Each node in our algorithm progressively adjusts its community label based on an even more restrictive level of locality, considering its neighbours local information solely. Our algorithm offers a computational complexity of linear order with respect to the network size. Experiments on both artificial and real networks show that our algorithm gains moreover networks with weak community structures compared to networks with strong community structures. Additionally, we provide experiments to demonstrate the ability of the self-defining source node of our algorithm by implementing various source node selection methods from the literature. [less ▲]

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See detailPrivacy and Security of Big Data in AI Systems:A Research and Standards Perspective
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in 2019 IEEE International Conference on Big Data (Big Data), 9-12 December 2019 (2020, February 24)

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See detailLink Definition ameliorating Community Detection in Collaboration Networks
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Piyatumrong, Apivadee et al

in Frontiers in Big Data (2019), 2

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See detailTransforming Collaboration Data into Network Layers for Enhanced Analytics
Esmaeilzadeh Dilmaghani, Saharnaz UL; Piyatumrong, Apivadee; Bouvry, Pascal UL et al

Scientific Conference (2019, February 25)

We consider the problem of automatically generating networks from data of collaborating researchers. The objective is to apply network analysis on the resulting network layers to reveal supplemental ... [more ▼]

We consider the problem of automatically generating networks from data of collaborating researchers. The objective is to apply network analysis on the resulting network layers to reveal supplemental patterns and insights of the research collaborations. In this paper, we describe our data-to-networks method, which automatically generates a set of logical network layers from the relational input data using a linkage threshold. We, then, use a series of network metrics to analyze the impact of the linkage threshold on the individual network layers. Moreover, results from the network analysis also provide beneficial information to improve the network visualization. We demonstrate the feasibility and impact of our approach using real-world collaboration data. We discuss how the produced network layers can reveal insights and patterns to direct the data analytics more intelligently. [less ▲]

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