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Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, 2004
Providing knowledge workers with access to experts and communities-of-practice is central to sharing expertise and crucial to organizational performance, adaptation, and even survival. This paper covers ongoing research to develop an Expert Locator prototype, a model-based system for detecting experts and broader communities-of-practice. The underlying
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Providing knowledge workers with access to experts and communities-of-practice is central to sharing expertise and crucial to organizational performance, adaptation, and even survival. This paper covers ongoing research to develop an Expert Locator prototype, a model-based system for detecting experts and broader communities-of-practice. The underlying
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2023
Abstract We critically review the application of community detection in archaeology, as well as its potential to be developed on archaeological big data. The challenges in applying community detection algorithms are presented with a reference case study from the Balkans.
Jelena Grujić, Miljana Radivojević
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Abstract We critically review the application of community detection in archaeology, as well as its potential to be developed on archaeological big data. The challenges in applying community detection algorithms are presented with a reference case study from the Balkans.
Jelena Grujić, Miljana Radivojević
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Scalable Overlapping Community Detection
2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2016Recent advancements in machine learning algorithms have transformed the data analytics domain and provided innovative solutions to inherently difficult problems. However, training models at scale over large data sets remains a daunting challenge. One such problem is the detection of overlapping communities within graphs.
Ismail El-Helw +5 more
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LGIEM: Global and local node influence based community detection
Future generations computer systems, 2020Community detection is one of the hot topics in the complex networks. It aims to find subgraphs that are internally dense but externally sparsely connected.
Tinghuai Ma +5 more
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Network Embedding for Community Detection in Attributed Networks
ACM Transactions on Knowledge Discovery from Data, 2020Community detection aims to partition network nodes into a set of clusters, such that nodes are more densely connected to each other within the same cluster than other clusters.
Heli Sun +8 more
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IEEE Transactions on Cybernetics, 2020
Evolutionary algorithms have been demonstrated to be very competitive in the community detection for complex networks. They, however, show poor scalability to large-scale networks due to the exponential increase of search space. In this paper, we suggest
Xing-yi Zhang +5 more
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Evolutionary algorithms have been demonstrated to be very competitive in the community detection for complex networks. They, however, show poor scalability to large-scale networks due to the exponential increase of search space. In this paper, we suggest
Xing-yi Zhang +5 more
semanticscholar +1 more source
Community Detection in Hypergraphs
2012Many datasets can be interpreted as graphs, i.e. as elements (nodes) and binary relations between them (edges). Under the label of complex network analysis, a vast array of graph-based methods allows the exploration of datasets purely based on such structural properties.
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Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation
Behavior Research Methods, 2023A. Christensen +3 more
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