oddball: Spotting Anomalies in Weighted Graphs

Akoglu, Leman; McGlohon, Mary; Faloutsos, Christos

doi:10.1007/978-3-642-13672-6_40

Cited by 422 publications

(372 citation statements)

References 23 publications

(16 reference statements)

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“…The algorithms rely on extracting graph-level (global) features and tracking these metrics over time. In [17], the authors propose to detect abnormal nodes from weighted graphs based on features and patterns from the egonet. There has also been work on using local and global structural features to improve the performance of network classifiers [18].…”

Section: Related Workmentioning

confidence: 99%

RolX: Role Extraction and Mining in Large Networks

Henderson¹,

Gallagher²,

Eliassi-Rad³

et al. 2011

197

287

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Abstract-Given a graph, how can we automatically discover roles for nodes? Roles could be, eg., 'bridges', or 'peripherynodes', etc. Roles are compact summaries of a node's behavior that generalize across networks. They enable numerous novel and useful network mining tasks, such as sense-making, searching for similar nodes, and node classification. We propose RolX (Role eXtraction), a scalable (linear in the number of edges), unsupervised learning approach for automatically extracting roles from general network data. We demonstrate the effectiveness of RolX on several network mining tasks, from exploratory data analysis to network transfer learning. Moreover, we compare network role discovery with network community discovery. We highlight fundamental differences between the two (e.g., roles generalize across disconnected networks, communities do not).

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Section: Related Workmentioning

confidence: 99%

RolX: Role Extraction and Mining in Large Networks

Henderson¹,

Gallagher²,

Eliassi-Rad³

et al. 2011

197

287

View full text Add to dashboard Cite

show abstract

“…• at which nodes or subgraphs to start the exploration [67], [57]? • if a user has a particular goal in mind, can it be predicted by their interactions?…”

Section: B Global and Local Viewsmentioning

confidence: 99%

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Pienta

Abello

Kahng

et al. 2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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Abstract-Making sense of large graph datasets is a fundamental and challenging process that advances science, education and technology. We survey research on graph exploration and visualization approaches aimed at addressing this challenge. Different from existing surveys, our investigation highlights approaches that have strong potential in handling large graphs, algorithmically, visually, or interactively; we also explicitly connect relevant works from multiple research fields -data mining, machine learning, human-computer ineraction, information visualization, information retrieval, and recommender systems -to underline their parallel and complementary contributions to graph sensemaking.We ground our discussion in sensemaking research; we propose a new graph sensemaking hierarchy that categorizes tools and techniques based on how they operate on the graph data (e.g., local vs global). We summarize and compare their strengths and weaknesses, and highlight open challenges. We conclude with future research directions for graph sensemaking.

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“…Akoglu et al [3] proposed a method to assign anomaly scores to nodes based on egonet properties in weighted networks. Our framework allows us to incorporate such properties as application-specific filters.…”

Section: Related Workmentioning

confidence: 99%

Overlapping Communities for Identifying Misbehavior in Network Communications

Moradi

Olovsson

Tsigas

2014

Advances in Knowledge Discovery and Data Mining

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Abstract. In this paper, we study the problem of identifying misbehaving network communications using community detection algorithms. Recently, it was shown that identifying the communications that do not respect community boundaries is a promising approach for network intrusion detection. However, it was also shown that traditional community detection algorithms are not suitable for this purpose. In this paper, we propose a novel method for enhancing community detection algorithms, and show that contrary to previous work, they provide a good basis for network misbehavior detection. This enhancement extends disjoint communities identified by these algorithms with a layer of auxiliary communities, so that the boundary nodes can belong to several communities. Although non-misbehaving nodes can naturally be in more than one community, we show that the majority of misbehaving nodes belong to multiple overlapping communities, therefore overlapping community detection algorithms can also be deployed for intrusion detection. Finally, we present a framework for anomaly detection which uses community detection as its basis. The framework allows incorporation of application-specific filters to reduce the false positives induced by community detection algorithms. Our framework is validated using large email networks and flow graphs created from real network traffic.

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oddball: Spotting Anomalies in Weighted Graphs

Cited by 422 publications

References 23 publications

RolX: Role Extraction and Mining in Large Networks

RolX: Role Extraction and Mining in Large Networks

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Overlapping Communities for Identifying Misbehavior in Network Communications

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