Individual and Collective Graph Mining
Principles, Algorithms, and Applications
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21 Result(s)
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Article
A hidden challenge of link prediction: which pairs to check?
The traditional setup of link prediction in networks assumes that a test set of node pairs, which is usually balanced, is available over which to predict the presence of links. However, in practice, there is n...
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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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Article
t-PINE: tensor-based predictable and interpretable node embeddings
Graph representations have increasingly grown in popularity during the last years. Existing representation learning approaches explicitly encode network structure. Despite their good performance in downstream ...
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Article
Fast network discovery on sequence data via time-aware hashing
Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, climate science, economics, and more. In domains where networks are discover...
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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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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
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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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
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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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...
