(Georgia Institute of Technology, 2016-04-15)
Tamersoy, Acar
Graphs (or networks) are now omnipresent, infusing into many aspects of society. This dissertation contributes unified graph-based algorithms and models to help solve large-scale societal problems affecting millions of individuals' daily lives, from cyber-attacks involving malware to tobacco and alcohol addiction. The main thrusts of our research are: (1) Propagation-based Graph Mining Algorithms: We develop graph mining algorithms to propagate information between the nodes to infer important details about the unknown nodes. We present three examples: AESOP (patented) unearths malware lurking in people's computers with 99.61% true positive rate at 0.01% false positive rate; our application of ADAGE on malware detection (patent-pending) enables to detect malware in a streaming setting; and EDOCS (patent-pending) flags comment spammers among 197 thousand users on a social media platform accurately and preemptively. (2) Graph-induced Behavior Characterization: We derive new insights and knowledge that characterize certain behavior from graphs using statistical and algorithmic techniques. We present two examples: a study on identifying attributes of smoking and drinking abstinence and relapse from an addiction cessation social media community; and an exploratory analysis of how company insiders trade. Our work has already made impact to society: deployed by Symantec, AESOP is protecting over 120 million people worldwide from malware; EDOCS has been deployed by Yahoo and it guards multiple online communities from comment spammers.
(Georgia Institute of Technology, 2013)
Pienta, Robert; Tamersoy, Acar; Tong, Hanghang; Chau, Duen Horng
Given a large graph with millions of nodes and edges, say a social
graph where both the nodes and edges can have multiple different
kinds of attributes (e.g., job titles, tie strengths), how do we quickly
find matches for subgraphs of interest (e.g., a ring of businessmen
with strong ties)? We propose MAGE,
Multiple Attribute Graph Engine, a subgraph matching framework that pushes the envelope of graph
matching capabilities and performance, through several major innovations: (i) with
line graph
transformation, MAGE works for graphs
with both node and edge attributes and return both exact as well as
near matches — other techniques often support only node attributes
and return only exact matches; (ii) MAGE supports a plethora of
queries, including multiple attributes for each node or edge, wild-cards as attribute values (i.e., match any
permissible value), and continuous attributes via multiple discretization strategies; (iii) MAGE
leverages a novel technique based on
memory mapping
to compute
random walk with restart probabilities, which provides a speedup
of more than
2 orders of magnitude on large graphs. We evaluated
MAGE’s effectiveness and scalability with real and synthetic graphs
with up to 2.3
million edges. Experimental results on the DBLP authorship graph and the Rotten Tomatoes movie graph illustrate the
effectiveness and exploratory functionality of our contributions to
graph querying. By devising query-centric innovations, our work improves the ease with which a user can explore their graph data.