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Data mining of social networks represented as graphs
In this survey we review the literature and concepts of the data mining of social networks,
with special emphasis on their representation as a graph structure. The survey is divided …
with special emphasis on their representation as a graph structure. The survey is divided …
Random graph modeling: A survey of the concepts
Random graph (RG) models play a central role in complex networks analysis. They help us
to understand, control, and predict phenomena occurring, for instance, in social networks …
to understand, control, and predict phenomena occurring, for instance, in social networks …
De-anonymizing social networks
Operators of online social networks are increasingly sharing potentially sensitive information
about users and their relationships with advertisers, application developers, and data …
about users and their relationships with advertisers, application developers, and data …
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for discussion in
everyday life. Formal statistical models for the analysis of network data have emerged as a …
everyday life. Formal statistical models for the analysis of network data have emerged as a …
Oddball: Spotting anomalies in weighted graphs
Given a large, weighted graph, how can we find anomalies? Which rules should be violated,
before we label a node as an anomaly? We propose the oddball algorithm, to find such …
before we label a node as an anomaly? We propose the oddball algorithm, to find such …
Bridging the gap: A pragmatic approach to generating insider threat data
The threat of malicious insider activity continues to be of paramount concern in both the
public and private sectors. Though there is great interest in advancing the state of the art in …
public and private sectors. Though there is great interest in advancing the state of the art in …
Pegasus: A peta-scale graph mining system implementation and observations
In this paper, we describe PEGASUS, an open source peta graph mining library which
performs typical graph mining tasks such as computing the diameter of the graph, computing …
performs typical graph mining tasks such as computing the diameter of the graph, computing …
Scalable anomaly ranking of attributed neighborhoods
Given a graph with node attributes, what neighborhoods are anomalous? To answer this
question, one needs a quality score that utilizes both structure and attributes. Popular …
question, one needs a quality score that utilizes both structure and attributes. Popular …
Polonium: Tera-scale graph mining and inference for malware detection
We present Polonium, a novel Symantec technology that detects malware through large-
scale graph inference. Based on the scalable Belief Propagation algorithm, Polonium infers …
scale graph inference. Based on the scalable Belief Propagation algorithm, Polonium infers …
Evaluating cooperation in communities with the k-core structure
Community sub graphs are characterized by dense connections or interactions among its
nodes. Community detection and evaluation is an important task in graph mining. A variety …
nodes. Community detection and evaluation is an important task in graph mining. A variety …