Ecole d'ingénieur et centre de recherche en Sciences du numérique

Characterization of L1-norm statistic for anomaly detection in Erdos Renyi graphs

Kadavankandy, Arun; Cottatellucci, Laura; Avrachenkov, Konstantin

CDC 2016, 55th IEEE Conference on Decision and Control, December 12-14, 2016, Las Vegas, USA

We describe a test statistic based on the L^1-norm of the eigenvectors of a modularity matrix to detect the presence of an embedded Erdos-Renyi (ER) subgraph inside a larger ER random graph. We make use of the properties of the asymptotic distribution of eigenvectors of random graphs to derive the distribution of the test statistic under certain conditions on the subgraph size and edge probabilities. We show that the distributions differ sufficiently for well defined ranges of subgraph sizes and edge probabilities of the background graph and the subgraph. This method can have applications where it is sufficient to know whether there is an anomaly in a given graph without the need to infer its location. The results we derive on the distribution of the components of the eigenvector may also be useful to detect the subgraph nodes.

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Titre:Characterization of L1-norm statistic for anomaly detection in Erdos Renyi graphs
Mots Clés:Subgraph detection, Erdos-Renyi, Detection and Estimation
Ville:Las Vegas
Département:Systèmes de Communication
Eurecom ref:5064
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Bibtex: @inproceedings{EURECOM+5064, doi = {}, year = {2016}, title = {{C}haracterization of {L}1-norm statistic for anomaly detection in {E}rdos {R}enyi graphs}, author = {{K}adavankandy, {A}run and {C}ottatellucci, {L}aura and {A}vrachenkov, {K}onstantin}, booktitle = {{CDC} 2016, 55th {IEEE} {C}onference on {D}ecision and {C}ontrol, {D}ecember 12-14, 2016, {L}as {V}egas, {USA}}, address = {{L}as {V}egas, {\'{E}}{TATS}-{UNIS}}, month = {12}, url = {} }
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