Individual and Collective Graph Mining
Principles, Algorithms, and Applications
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Chapter and Conference Paper
Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms
If several friends of Smith have committed petty thefts, what would you say about Smith? Most people would not be surprised if Smith is a hardened criminal. Guilt-by-association methods combine weak signals to de...
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Chapter and Conference Paper
Patterns amongst Competing Task Frequencies: Super-Linearities, and the Almond-DG Model
If Alice has double the friends of Bob, will she also have double the phone-calls (or wall-postings, or tweets)? Our first contribution is the discovery that the relative frequencies obey a power-law (sub-line...
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Chapter and Conference Paper
Net-Ray: Visualizing and Mining Billion-Scale Graphs
How can we visualize billion-scale graphs? How to spot outliers in such graphs quickly? Visualizing graphs is the most direct way of understanding them; however, billion-scale graphs are very difficult to visu...
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Chapter and Conference Paper
Influence Propagation: Patterns, Model and a Case Study
When a free, catchy application shows up, how quickly will people notify their friends about it? Will the enthusiasm drop exponentially with time, or oscillate? What other patterns emerge?
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Chapter and Conference Paper
Com2: Fast Automatic Discovery of Temporal (‘Comet’) Communities
Given a large network, changing over time, how can we find patterns and anomalies? We propose Com2, a novel and fast, incremental tensor analysis approach, which can discover both transient and periodic/ repea...
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Article
Graph based anomaly detection and description: a survey
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past y...
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Article
Discovery of “comet” communities in temporal and labeled graphs Com \(^2\)
While the analysis of unlabeled networks has been studied extensively in the past, finding patterns in different kinds of labeled graphs is still an open challenge. Given a large edge-labeled network, e.g., a ...
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Article
Facebook wall posts: a model of user behaviors
How do people interact with their Facebook wall? At a high level, this question captures the essence of our work. While most prior efforts focus on Twitter, the much fewer Facebook studies focus on the friends...
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Chapter
Introduction
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Chapter
Conclusions and Further Research Problems
Graphs are very powerful representations of data and the relations among them. The Web, friendships and communications, collaborations and phone calls, traffic flow, or brain functions are only few examples of...
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Chapter
Summarization of Static Graphs
One natural way to understand a graph and its underlying processes is to visualize and interact with it. However, for large datasets with several millions or billions of nodes and edges, such as the Facebook s...
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Chapter
Summarization of Dynamic Graphs
In many applications, it is necessary or at least beneficial to explore multiple graphs collectively. These graphs can be temporal instances of the same set of objects (time-evolving graphs), or disparate netw...
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Book
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Chapter
Graph Alignment
Can we spot the same people in two different social networks, such as LinkedIn and Facebook? How can we find similar people across different graphs? How can we effectively link an information network with a socia...
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Chapter
Inference in a Graph
In Chapter 2 we saw how we can summarize a large graph and gain insights into its important and semantically meaningful structures. In this chapter we examine how we can use the network effects to learn about ...
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Chapter
Graph Similarity
A question that often comes up when studying multiple networks is: How much do two graphs or networks differ in terms of connectivity, and which are the main node and edge culprits for their difference? For ex...
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Chapter and Conference Paper
HashAlign: Hash-Based Alignment of Multiple Graphs
Fusing or aligning two or more networks is a fundamental building block of many graph mining tasks (e.g., recommendation systems, link prediction, collective analysis of networks). Most past work has focused o...
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Article
Reducing large graphs to small supergraphs: a unified approach
Summarizing a large graph with a much smaller graph is critical for applications like speeding up intensive graph algorithms and interactive visualization. In this paper, we propose CONditional Diversified Net...
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Article
On effective and efficient graph edge labeling
Graphs, such as social, road and information networks, are ubiquitous as they naturally model entities and their relationships. Many query processing tasks on graphs are concerned about efficiently accessing n...
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Article
Collaborative topic regression for predicting topic-based social influence
The rapid growth of social networks and their strong presence in our lives have attracted many researchers in social networks analysis. Users of social networks spread their opinions, get involved in discussio...
