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Chapter and Conference Paper
SpecGreedy: Unified Dense Subgraph Detection
How can we effectively detect fake reviews or fraudulent connections on a website? How can we spot communities that suddenly appear based on users’ interaction? And how can we efficiently find the minimum cut ...
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Chapter and Conference Paper
node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching
Identity stitching, the task of identifying and matching various online references (e.g., sessions over different devices and timespans) to the same user in real-world web services, is crucial for personalizatio...
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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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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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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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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
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...