Online Clustering Algorithms for Semantic-Rich Network Trajectories

Roh, Gook-Pil; Hwang, Seung-won

doi:10.5626/jcse.2011.5.4.346

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…The state-of-the-art methods use only one of the two aspects. The S-Cluster algorithm is a baseline clustering algorithm that only considers topological structure [4,7]. The other baseline algorithm K-SNAP partitions a graph such that each partition has nodes with identical attribute values [5].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Clustering Approach for link prediction in Heterogeneous Information Networks

Sajjadi

Esmaeili

Ghobaei‐Arani

et al. 2023

Preprint

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In recent years, researchers from academic and industrial fields have become increasingly interested in social network data to extract meaningful information. This information is used in applications such as link prediction between people groups, community detection, protein module identification, etc. Therefore, the clustering technique has emerged as a solution to finding similarities between social network members. Recently, in most graph clustering solutions, the structural similarity of nodes is combined with their attribute similarity. The results of these solutions indicate that the graph's topological structure is more important. Since most social networks are sparse, these solutions often suffer from insufficient use of node features. This paper proposes a hybrid clustering approach for link prediction in heterogeneous information networks (HINs). In our approach, an adjacency vector is determined for each node until, in this vector, the weight of the direct edge or the weight of the shortest communication path among every pair of nodes is considered. A similarity metric is presented that calculates similarity using the direct edge weight between two nodes and the correlation between their adjacency vectors. Finally, we evaluated the effectiveness of our proposed method using DBLP and Political blogs datasets under entropy, density, purity, and execution time metrics. The simulation results demonstrate that while maintaining the cluster density significantly reduces the entropy and the execution time compared with the other methods.

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Section: Introductionmentioning

confidence: 99%

“…The S-Cluster algorithm is a baseline clustering algorithm that only considers topological structure [4,7]. The other baseline algorithm K-SNAP partitions a graph such that each partition has nodes with identical attribute values [5]. In other words, the similarity of objects is measured based on only one of two aspects.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Clustering Approach for link prediction in Heterogeneous Information Networks

Sajjadi

Esmaeili

Ghobaei‐Arani

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Graph clustering is materialized as an interesting and challenging research area due to complex structures and relationships among entities in the real world. Consequently, efforts have been made on different aspects of graph clustering, [6,9,21,25], to have better insight and understanding from network structure and semantics.…”

Section: Introductionmentioning

confidence: 99%

Intra graph clustering using collaborative similarity measure

Nawaz

Khan

Lee

et al. 2015

Distrib Parallel Databases

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Graph is an extremely versatile data structure in terms of its expressiveness and flexibility to model a range of real life phenomenon. Various networks like social networks, sensor networks and computer networks are represented and stored in the form of graphs. The analysis of these kind of graphs has an immense importance from quite a long time. It is performed from various aspects to get maximum out of such multifaceted information repository. When the analysis is targeted towards finding groups of vertices based on their similarity in a graph, clustering is the most conspicuous option. Previous graph clustering approaches either focus on the topological structure or attributes likeness, however, few recent methods constitutes both aspects simultaneously. Due to enormous computation requirements for similarity estimation, these methods are often suffered from scalability issues. In order to overcome this limitation, we introduce collaborative similarity measure (CSM) for intra-graph clustering. CSM is based on shortest path strategy, instead of all paths, to define structural and semantic relevance among vertices. First, we calculate the pair-wise similarity among vertices using CSM. Second, vertices are grouped together based on calculated similarity under k-Medoid framework. Empirical analysis, based on density, and entropy, proves the efficacy of CSM over existing measures. Moreover, CSM becomes 123 Distrib Parallel Databases a potential candidate for medium scaled graph analysis due to an order of magnitude less computations.

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