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Books > Computing & IT > Applications of computing > Databases > Data mining
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Individual and Collective Graph Mining - Principles, Algorithms, and Applications (Paperback)
Loot Price: R1,727
Discovery Miles 17 270
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Individual and Collective Graph Mining - Principles, Algorithms, and Applications (Paperback)
Series: Synthesis Lectures on Data Mining and Knowledge Discovery
Supplier out of stock. If you add this item to your wish list we will let you know when it becomes available.
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Graphs naturally represent information ranging from links between
web pages, to communication in email networks, to connections
between neurons in our brains. These graphs often span billions of
nodes and interactions between them. Within this deluge of
interconnected data, how can we find the most important structures
and summarize them? How can we efficiently visualize them? How can
we detect anomalies that indicate critical events, such as an
attack on a computer system, disease formation in the human brain,
or the fall of a company? This book presents scalable, principled
discovery algorithms that combine globality with locality to make
sense of one or more graphs. In addition to fast algorithmic
methodologies, we also contribute graph-theoretical ideas and
models, and real-world applications in two main areas: Individual
Graph Mining: We show how to interpretably summarize a single graph
by identifying its important graph structures. We complement
summarization with inference, which leverages information about few
entities (obtained via summarization or other methods) and the
network structure to efficiently and effectively learn information
about the unknown entities. Collective Graph Mining: We extend the
idea of individual-graph summarization to time-evolving graphs, and
show how to scalably discover temporal patterns. Apart from
summarization, we claim that graph similarity is often the
underlying problem in a host of applications where multiple graphs
occur (e.g., temporal anomaly detection, discovery of behavioral
patterns), and we present principled, scalable algorithms for
aligning networks and measuring their similarity. The methods that
we present in this book leverage techniques from diverse areas,
such as matrix algebra, graph theory, optimization, information
theory, machine learning, finance, and social science, to solve
real-world problems. We present applications of our exploration
algorithms to massive datasets, including a Web graph of 6.6
billion edges, a Twitter graph of 1.8 billion edges, brain graphs
with up to 90 million edges, collaboration, peer-to-peer networks,
browser logs, all spanning millions of users and interactions.
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