During the last decade or so, the amount of data that is generated and becomes publicly available is rapidly growing. This makes it impossible to extract useful information from this huge amount of data manually without using automatic tools and algorithms. Data mining has been defined as the process of automatic extraction of useful and previously unknown information from a large dataset using techniques from statistics, artificial intelligence and database management. Furthermore, in many applications, such as Bioinformatics, the world wide web, social and technological and communication networks, data are usually represented with graphs. This makes graph mining practically interesting, while also a challenging research area, due to high computational cost involved in processing graph data. In this dissertation, we investigate two key problems in graph mining: frequent pattern mining and betweenness centrality computation. Existing algorithms for finding frequent patterns from large single networks mainly use subgraph isomorphism. However, subgraph isomorphism is expensive to compute: deciding whether one graph is subgraph isomorphic to another graph is NP-complete in terms of the sizes of the graphs. Recently, a few algorithms have used subgraph homomorphism. However, they find very restricted classes of patterns such as trees. The main challenge with pattern mining under subgraph homomorphism is the pattern generation phase. In this work, we go beyond trees and propose an efficient algorithm for mining graph patterns from large networks under homomorphism. We introduce a new class of patterns, called rooted graphs, and present an algorithm for complete generation of rooted graphs. We also propose a new data structure for compact representation of all frequent patterns. By performing extensive experiments on several real-world and synthetic large networks, we show the empirical efficiency of our proposed algorithm, called HoPa. We then present an efficient algorithm for subtree homeomorphism with application to frequent pattern mining. We propose a compact data-structure, called occ, that can encode and represent several occurrences of a tree pattern and define efficient join operations on the occ data-structure, that help us to count occurrences of tree patterns

https://lirias.kuleuven.be/bitstream/123456789/520255/1/thesis.pdf

A Theory-based Study of Graph Mining

MIPS: A graph mining library

https://lirias.kuleuven.be/bitstream/123456789/501314/1/abstract.pdf

Trypsin is the workhorse protease in mass spectrometry-based proteomics experiments and is used to digest proteins into more readily analyzable peptides. To identify these peptides after mass spectrometric analysis, the actual digestion has to be mimicked as faithfully as possible in silico. In this paper we introduce CP-DT (Cleavage Prediction with Decision Trees), an algorithm based on a decision tree ensemble that was learned on publicly available peptide identification data from the PRIDE repository. We demonstrate that CP-DT is able to accurately predict tryptic cleavage: tests on three independent data sets show that CP-DT significantly outperforms the Keil rules that are currently used to predict tryptic cleavage. Moreover, the trees generated by CP-DT can make predictions efficiently and are interpretable by domain experts.

/pdf/predicting-tryptic-cleavage-from-proteomics-data-using-3vqxq0n8se.pdf

Predicting Tryptic Cleavage from Proteomics Data Using Decision Tree Ensembles

Mining frequent patterns in a single network (graph) poses a number of challenges. Already only to match one path pattern to a network under subgraph isomorphism is NP-complete. Classical matching algorithms become intractable even for reasonably small patterns, on networks which are large or have a high average degree. Based on recent advances in parameterized complexity theory, we propose a novel miner for rooted trees in networks. The miner, for a fixed parameter $$k$$
 (maximal pattern size), can mine all rooted trees with delay linear in the size of the network and only mildly exponential in the fixed parameter $$k$$
 . This allows us to mine tractably, rooted trees, in large networks such as the WWW or social networks. We establish the practical applicability of our miner, by presenting an experimental evaluation on both synthetic and real-world data.

Nearly exact mining of frequent trees in large networks

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MIPS: A graph mining library

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A Theory-based Study of Graph Mining

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Predicting Tryptic Cleavage from Proteomics Data Using Decision Tree Ensembles

Nearly exact mining of frequent trees in large networks

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