Clustering large attributed information networks: an efficient incremental computing approach

Cheng, Hong; Zhou, Yang; Huang, Xin; Yu, Jeffrey Xu

doi:10.1007/s10618-012-0263-0

Cited by 42 publications

(25 citation statements)

References 23 publications

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“…As expected from the construction of G A , SA-Cluster is computationally expensive, namely, its time complexity is O(n 3 ). In order to improve the efficiency and scalability of SA-Cluster, the methods Inc-Cluster [39,232] and SA-Cluster-Opt [40] have been proposed. The main idea behind them is to reduce the number and the complexity of random walk distance computations.…”

Section: 3mentioning

confidence: 99%

Community Detection in Attributed Social Networks: A Unified Weight-Based Model and Its Regimes

Chunaev

Nuzhdenko

Bochenina

2019

2019 International Conference on Data Mining Workshops (ICDMW)

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Community detection is a fundamental problem in social network analysis consisting, roughly speaking, in dividing social actors (modelled as nodes in a social graph) with certain social connections (modelled as edges in the social graph) into densely knitted and highly related groups with each group well separated from the others. Classical approaches for community detection usually deal only with the structure of the network and ignore features of the nodes, although major real-world networks provide additional actors' information such as age, gender, interests, etc., traditionally called node attributes. It is known that the attributes may clarify and enrich the knowledge about the actors and give sense to the detected communities. This has led to a relatively novel direction in community detection -constructing algorithms that use both the structure and the attributes of the network (modelled already via a nodeattributed graph) to yield more informative and qualitative results.During the last decade many methods based on different ideas and techniques have appeared in this direction. Although there exist some partial overviews of them, a recent survey is a necessity as the growing number of the methods may cause uncertainty in practice.In this paper we aim at clarifying the overall situation by proposing a clear classification of the methods and providing a comprehensive survey of the available results. We not only group and analyse the corresponding methods but also focus on practical aspects, including the information which methods outperform others and which datasets and quality measures are used for evaluation.

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Section: 3mentioning

confidence: 99%

Community Detection in Attributed Social Networks: A Unified Weight-Based Model and Its Regimes

Chunaev

Nuzhdenko

Bochenina

2019

2019 International Conference on Data Mining Workshops (ICDMW)

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“…Then, a clustering process is performed using random-walk-based node similarity [7]. The Inc-Cluster (incremental SA-Cluster) improve SA-Cluster by computing node similarity gradually according to the change of attribute weight [9]. Such approaches take into account node attributes in a novel way but the augmented graph will become much more complex when the number of attribute values is large, so they are not suitable for complex social networks.…”

Section: Non-overlapping Community Detection Algorithmsmentioning

confidence: 99%

An Improved Random Walk Based Community Detection Algorithm

Ji¹,

Sun²,

Li³

2014

IJMUE

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“…Images in each subgraph represent a concept-preserving cluster (Recall from Section 1). Note that state-of-the-art graph clustering techniques [1,3,13] cannot be directly leveraged to identify these clusters as they do not preserve concepts, typically generate non-overlapping clusters, and do not maximally cover the entire graph. The summary compression process "compresses" S to form a summary at reduced level of detail (denoted by V).…”

Section: The Prism Algorithmmentioning

confidence: 99%

Prism

Seah

Bhowmick

Sun

2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Most existing tag-based social image search engines present search results as a ranked list of images, which cannot be consumed by users in a natural and intuitive manner. In this paper, we present a novel concept-preserving image search results summarization algorithm named prism. prism exploits both visual features and tags of the search results to generate high quality summary, which not only breaks the results into visually and semantically coherent clusters but it also maximizes the coverage of the summary w.r.t the original search results. It first constructs a visual similarity graph where the nodes are images in the search results and the edges represent visual similarities between pairs of images. This graph is optimally decomposed and compressed into a set of concept-preserving subgraphs based on a set of summarization objectives. Images in a concept-preserving subgraph are visually and semantically cohesive and are described by a minimal set of tags or concepts. Lastly, one or more exemplar images from each subgraph is selected to form the exemplar summary of the result set. Through empirical study, we demonstrate the effectiveness of prism against state-ofthe-art image summarization and clustering algorithms.

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Clustering large attributed information networks: an efficient incremental computing approach

Cited by 42 publications

References 23 publications

Community Detection in Attributed Social Networks: A Unified Weight-Based Model and Its Regimes

Community Detection in Attributed Social Networks: A Unified Weight-Based Model and Its Regimes

An Improved Random Walk Based Community Detection Algorithm

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