EdgeCentric: Anomaly Detection in Edge-Attributed Networks
release_mmqhugfqnvewzdyppmfabradn4
by
Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Gunnemann,
Disha Makhija, Mohit Kumar, Christos Faloutsos
2015
Abstract
Given a network with attributed edges, how can we identify anomalous
behavior? Networks with edge attributes are commonplace in the real world. For
example, edges in e-commerce networks often indicate how users rated products
and services in terms of number of stars, and edges in online social and
phonecall networks contain temporal information about when friendships were
formed and when users communicated with each other -- in such cases, edge
attributes capture information about how the adjacent nodes interact with other
entities in the network. In this paper, we aim to utilize exactly this
information to discern suspicious from typical node behavior. Our work has a
number of notable contributions, including (a) formulation: while most other
graph-based anomaly detection works use structural graph connectivity or node
information, we focus on the new problem of leveraging edge information, (b)
methodology: we introduce EdgeCentric, an intuitive and scalable
compression-based approach for detecting edge-attributed graph anomalies, and
(c) practicality: we show that EdgeCentric successfully spots numerous such
anomalies in several large, edge-attributed real-world graphs, including the
Flipkart e-commerce graph with over 3 million product reviews between 1.1
million users and 545 thousand products, where it achieved 0.87 precision over
the top 100 results.
In text/plain
format
Archived Files and Locations
application/pdf 742.6 kB
file_vaezsnrkgbfa7dyixuyuoriezy
|
arxiv.org (repository) web.archive.org (webarchive) |
1510.05544v2
access all versions, variants, and formats of this works (eg, pre-prints)