000263213 001__ 263213
000263213 005__ 20190812204756.0
000263213 037__ $$aCONF
000263213 245__ $$aAnomaly detection in the dynamics of web and social networks
000263213 260__ $$c2019
000263213 269__ $$a2019
000263213 300__ $$a10
000263213 336__ $$aConference Papers
000263213 520__ $$aIn this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an anomaly as a localized increase in temporal activity in a cluster of nodes. The algorithm is unsupervised. It is able to detect and track anomalous activity in a dynamic network despite the noise from multiple interfering sources. We use the Hopfield network model of memory to combine the graph and time information. We show that anomalies can be spotted with good precision using a memory network. The presented approach is scalable and we provide a distributed implementation of the algorithm. To demonstrate its efficiency, we apply it to two datasets: Enron Email dataset and Wikipedia page views. We show that the anomalous spikes are triggered by the real-world events that impact the network dynamics. Besides, the structure of the clusters and the analysis of the time evolution associated with the detected events reveals interesting facts on how humans interact, exchange and search for information, opening the door to new quantitative studies on collective and social behavior on large and dynamic datasets.
000263213 542__ $$fCC BY
000263213 6531_ $$aAnomaly Detection
000263213 6531_ $$aDynamic Network
000263213 6531_ $$aGraph Algorithm
000263213 6531_ $$aHopfield Network
000263213 6531_ $$aWikipedia
000263213 6531_ $$aWeb Logs Analysis
000263213 700__ $$g255928$$aMiz, Volodymyr$$0249844
000263213 700__ $$g229699$$aRicaud, Benjamin$$0246772
000263213 700__ $$aBenzi, Kirell$$0245769$$g204172
000263213 700__ $$0240428$$aVandergheynst, Pierre$$g120906
000263213 7112_ $$aThe Web Conference 2019$$cSan Francisco, California, USA$$dMay 13-17, 2019
000263213 790__ $$2url$$whttps://zenodo.org/record/886951
000263213 790__ $$2url$$whttps://zenodo.org/record/886484
000263213 790__ $$2url$$whttps://zenodo.org/record/1342353
000263213 8560_ $$fvolodymyr.miz@epfl.ch
000263213 8564_ $$zPREPRINT$$uhttps://infoscience.epfl.ch/record/263213/files/Anomaly%20detection%20in%20the%20dynamics%20of%20web%20and%20social%20networks.pdf$$s4796203
000263213 8564_ $$uhttps://infoscience.epfl.ch/record/263213/files/Fixed%20typo.pdf$$s4796141
000263213 8564_ $$uhttps://infoscience.epfl.ch/record/263213/files/Anomaly%20detection%20in%20the%20dynamics%20of%20web%20and%20social%20networks%20v1.pdf$$s4796141
000263213 909C0 $$xU10380$$pLTS2$$mpierre.vandergheynst@epfl.ch$$0252392$$zMarselli, Béatrice
000263213 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.epfl.ch:263213
000263213 960__ $$avolodymyr.miz@epfl.ch
000263213 961__ $$apierre.devaud@epfl.ch
000263213 973__ $$aEPFL$$rREVIEWED
000263213 980__ $$aCONF
000263213 981__ $$aoverwrite