MetricForensics: A Multi-Level Approach for Mining Volatile Graphs

Henderson, Keith; Eliassi-Rad, Tina; Faloutsos, Christos; Akoglu, Leman; Li, Lei; Maruhashi, Koji; Prakash, B. Aditya; Tong, Hanghang

doi:10.2172/1114747

Cited by 26 publications

(27 citation statements)

References 24 publications

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“…In [15], the authors propose extracting topological features for pairs of nodes for link prediction. In [16], the authors develop a multi-level framework to detect anomalies in time-varying graphs based on graph, sub-graph, and node level features. The algorithms rely on extracting graph-level (global) features and tracking these metrics over time.…”

Section: Related Workmentioning

confidence: 99%

RolX: Role Extraction and Mining in Large Networks

Henderson¹,

Gallagher²,

Eliassi-Rad³

et al. 2011

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204

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

Self Cite

204

287

View full text Add to dashboard Cite

show abstract

“…These approaches are categorized in 4 parts as follows: 1) Feature-based approaches: the key idea of these approaches is based on the concept that similar graphs probably share common attributes such as diameter, eigenvalues, and a distribution of degree. Moreover, these methods can be used for checking the structure of a graph in order to find patterns and explore anomalies [15][16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Intrusion detection in network flows based on an optimized clustering criterion

Karimpour¹,

Lotfi²,

Siahmarzkooh³

2017

Turk J Elec Eng & Comp Sci

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Abstract:Graph-based intrusion detection approaches consider the network as a graph and detect anomalies based on graph metrics. However, most of these approaches succumb to the cluster-based behavior of the anomalies. To resolve this problem in our study, we use flow and graph-clustering concepts to create a data set first. A new criterion related to the average weight of clusters is then defined and a model is proposed to detect attacks based on the above-mentioned criterion. Finally, the model is evaluated using a DARPA data set. Results show that the proposed approach detects the attacks with high accuracy relative to methods described in previous studies.

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“…Several techniques have been developed to analyze such ensembles [6,22,54,117]. Drawing a parallel with graph mining, it is interesting to detect communities that are persistent across the ensemble [4].…”

Section: Motivation Behind This Thesismentioning

confidence: 99%

“…We choose four commonly used structural properties [55] to understand the differences among the networks in a network ensemble (Table 5.2). We notice that the value of each structural property typically vary from one network to the other.…”

Section: Discovering Network Sensorsmentioning

confidence: 99%

Unstable Communities in Network Ensembles

Rahman

Jan

Kim³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Ensembles of graphs arise naturally in many applications, for example, the temporal evolution of social contacts or computer communications, tissue-specific protein interaction networks, annual citation or co-authorship networks in a field, or a family of high-likelihood Bayesian networks inferred from systems biology data. Several techniques have been developed to analyze such ensembles. A canonical problem is that of computing communities that are persistent across the ensemble. This problem is usually formulated as one of computing dense subgraphs (communities) that are frequent, i.e., appear in many graphs in the ensemble.In this thesis, we seek to find "unstable communities," which are the antithesis of frequent, dense subgraphs. Informally, an unstable community is a set of nodes that induces highly-varying subgraphs in the ensemble. In other words, the graphs in the ensemble disagree about the precise pairwise connections among these nodes. The primary contribution of this dissertation is to introduce the concept of unstable communities as a novel problem in the field of graph mining. Specifically, it presents three approaches to mathematically formulate the concept of unstable communities, devises algorithms for computing such communities in a given ensemble of networks, and shows the usefulness of this concept in a variety of settings.Our first definition of unstable community relies on two parameters: the first ensures that a node set induces several different subgraphs in the ensemble and the second guarantees that each of these subgraphs occurs in a large number of graphs in the ensemble. We present two algorithms to enumerate unstable communities that match this definition. The first approach, ClustMiner, is a heuristic that transforms the problem into one of computing dense subgraphs in a single graph that summarizes the ensemble. The second approach, UCMiner, is guaranteed to enumerate all maximal unstable communities correctly. We apply both approaches to systems biology datasets to demonstrate that UCMiner is superior to ClustMiner in the sense that ClustMiner's output contains node sets that are not unstable while also missing several communities computed by UCMiner. We find several node sets that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns.Our second and third definitions of unstable community rely on a novel concept of (scaled) subgraph divergence, a formulation that uses the concept of relative entropy to measure the instability of a community. We propose another algorithm, SDMiner, that can exactly enumerate all maximal unstable communities with small (scaled) subgraph divergence. We perform extensive experiments on social network datasets to show that we can discover UCs that capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets.Dedicated to the memory of my spiritual ...

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MetricForensics: A Multi-Level Approach for Mining Volatile Graphs

Cited by 26 publications

References 24 publications

RolX: Role Extraction and Mining in Large Networks

RolX: Role Extraction and Mining in Large Networks

Intrusion detection in network flows based on an optimized clustering criterion

Unstable Communities in Network Ensembles

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