SCAN: a structural clustering algorithm for networks

By Xiaowei Xu, Nurcan Yuruk, T. Schweiger. Knowledge Discovery and Data Mining, 2007

949 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/25dfac6955913c163a61bc6e2ae8c5c7f3ca8f87

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Charu C. Aggarwal
2. Philip S. Yu
3. Chengqi Zhang
4. Jiawei Han
5. Huan Liu
6. Shirui Pan
7. Xingquan Zhu
8. Hong Cheng

In Molecular Biology

1. Shirui Pan
2. Chuan Zhou
3. Chengqi Zhang
4. Leman Akoglu
5. Danai Koutra
6. Philip S. Yu
7. Huan Liu
8. Jiawei Han

In Statistical and Nonlinear Physics

1. Charu C. Aggarwal
2. Huan Liu
3. Chengqi Zhang
4. Bryan Perozzi
5. Jiawei Han
6. Yizhou Sun
7. Shirui Pan
8. Jundong Li

Citations in the corpus

• Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021, background): “Structural clustering algorithm for networks (SCAN) [89] is one of the successful density-based clustering algorithms for a graph, a fundamental data structure.”
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (2021, background): “Such diversity makes it difficult to apply anomaly detection methods for attribute-only data (e.g., OC-SVM [14]) or plain networks (e.g., LOF [15]) to attributed networks directly.”
• Ranking-based clustering of heterogeneous information networks with star network schema (2009, background): “Rather than investigate the global structure like spectral clustering, several density-based methods [19, 21] are proposed to find clusters in networks which utilizes some neighborhood information for each object.”
• Community-based greedy algorithm for mining top-K influential nodes in mobile social networks (2010, background): “There exist a number of algorithms [3,8,13,18,20, 21, 23, 24] for community detection.”
• Graph based anomaly detection and description: a survey (2014, methodology): “To overcome the issue of selecting the minimum similarity threshold parameter of [Xu et al., 2007], [Sun et al.”
• PathSim (2011, background): “SCAN [20] measures similarity of two objects by comparing their immediate neighbor sets.”
• Radar: Residual Analysis for Anomaly Detection in Attributed Networks (2017, methodology): “Among them, LOF, SCAN, CODA covers three types of widely defined anomalies in attributed networks (contextual anomaly, structural anomaly and community anomaly).”
• A Comprehensive Survey on Community Detection With Deep Learning (2021, background): “Significant clustering algorithms include density-based spatial clustering of applications with noise (DBSCAN) [51], structural clustering algorithm for networks (SCAN) [52], and locating structural centers for community detection (LCCD) [53].”
• Efficient aggregation for graph summarization (2008, methodology): “Various graph partitioning algorithms [14, 18, 22] are used to detect community structures (dense subgraphs) in large graphs.”
• Community detection in Social Media (2012, methodology): “Examples of such community definitions are cliques, n-cliques, n-clubs, n-clans, k-plexes, and k-cores (Wasserman and Faust 1994; Scott 2000).”
• Graph Clustering Based on Structural/Attribute Similarities (2009, methodology): “Graph clustering is an interesting and challenging research problem which has received much attention recently [16,19, 26].”
• Graph Embedding Techniques, Applications, and Performance: A Survey (2017, background): “Structural equivalence clustering [50], on the contrary, is designed to identify nodes with similar roles (like bridges and outliers).”
• Network Representation Learning: A Survey (2017, background): “, [79]) try to identify nodes with similar structural roles like bridges and outliers.”
• Sensing Trending Topics in Twitter (2013, methodology): “Its unique feature is that for the feature clustering step it uses the Structural Clustering Algorithm for Networks (SCAN) [40].”
• Deep Anomaly Detection on Attributed Networks (2019, background): “• SCAN [34] which detects anomalies at the structural level.”
• ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation (2025, unknown): “We propose an iterative LLM-based hierarchical clustering framework that supports arbitrary graph augmentation (e.g., KNN connections and CODI-CIL [53]) and graph clustering algorithms (e.g., weighted Leiden [62], weighted spectral clustering, and SCAN [73]).”