Neighborhood formation and anomaly detection in bipartite graphs

By Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Christos Faloutsos. Industrial Conference on Data Mining, 2005

375 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e028820ff569c59338fb7df22484d2435e194d0b

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In Artificial Intelligence

In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Contextual Outlier Interpretation (2017, methodology): “ each cluster corresponds to one aspect of the context. Such an idea of context clustering is inspired by various anomaly detection models which perform data clustering prior to recognizing anomalies [17, 30, 39, 45]. To determine the number of clusters L in Ci, we adopt the measure of prediction st”
Anomaly detection: A survey (2009, methodology): “2006], detecting faults in web applications [Ide and Kashima 2004; Sun et al. 2005], detecting anomalies in biological data [Kadota et al.”
Random walk with restart: fast solutions and applications (2008, methodology): “Other methods sharing this requirement include regularized regression, Gaussian process regression [27], and so on. Existing fast solution for RWR include Hub-vector decomposition based [16]; block structure based [18, 28 ]; fingerprint based [9], and so on. Many applications take random walk and re”
Fast Random Walk with Restart and Its Applications (2006, methodology): “Based on this observation, combined with the factor that many re l graphs has blockwise/community structure, the authors in [25] proposed performing RWR only on the partition that contains the starting pointi (methodBlk).”
Outlier Analysis (2013, methodology): “Some of these methods [429, 431] are specifically applicable to bipartite graphs.”
Graph based anomaly detection and description: a survey (2014, background): “Approaches: Methods that exploit communities or proximity of nodes in the graph to spot (node) anomalies in bipartite graphs include [Sun et al., 2005].”
Practical Attacks Against Graph-based Clustering (2017, background): “The first, targeted noise injection, improves on random injections [34, 54] by emulating the legitimate signal’s graph structure.”
Generative Image Segmentation Using Random Walks with Restart (2008, methodology): “It has good performance on many other applications: Cross-model correlation discovery [8], Center-piece subgraph discovery [13], Content based image retrieval [12], Neighborhood formulation [14] and etc.”
Audience selection for on-line brand advertising: privacy-friendly social network targeting (2009, background): “If more sophisticated techniques [14, 18, 19, 25, 26] can scale, they may provide the basis for improving the results we present below.”
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks (2018, neutral): “We first learn the latent network representations by using a number of network walks extracted from the initial network.”