Efficient MinHash-based algorithms for big structured data

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Nowadays, data are growing explosively in the information society. Every day, an incredibly large number of data are generated from social networks, financial industries, medical device and digital equipment, etc. – Big data have been driving data mining research in both academia and industry. No matter how data mining and machine learning develop, in most tasks such as classification, clustering and retrieval, it is one of the most fundamental operations to compute the similarity of data. However, exact similarity computation has become daunting for big data due to the “3V” nature (volume, velocity and variety). Therefore, the thesis aims to develop a number of efficient randomized hashing algorithms for big structured data to approximate the similarity. Taking into account the popularity and importance of data modelled as weighted sets, graphs/networks, the MinHash-based algorithms are proposed to transform weighted sets, graphs and network nodes into low-dimensional hash codes for efficient similarity estimation. In Chapter 4, we propose two MinHash-based algorithms for weighted sets, which are two instances of the Consistent Weighted Sampling (CWS) scheme, for real-value weighted sets by uncovering the working mechanism of the state-of-the-art weighted MinHash algorithm, Improved Consistent Weighted Sampling (ICWS) algorithm. The two algorithms both represent a weighted set as a low-dimensional hash code. The first algorithm, the Canonical Consistent Weighted Sampling (CCWS) algorithm, reconstructs the hash functions in ICWS in order to overcome the flaw that ICWS breaks the uniformity of the MinHash scheme. Consequently, our CCWS algorithm not only shares the same theoretical computational complexity as ICWS but also strictly complies with the definition of the MinHash algorithm. The second algorithm, the Practical Consistent Weighted Sampling (PCWS) algorithm, simplifies ICWS by transforming the original form of ICWS into an equivalent expression. As a result, our PCWS algorithm is not only mathematically equivalent to ICWS and preserves the same theoretical properties, but also saves 20% memory footprint and substantial computational cost compared to ICWS. In Chapter 5, we propose a MinHash-based algorithm for graphs, i.e., the K-Ary Tree Hashing (KATH) algorithm, for representing a graph as a low-dimensional feature vector. The main idea of KATH is to construct a traversal table to quickly approximate the subtree patterns in Weisfeiler-Lehman (WL) graph kernel using K-ary trees. Based on the traversal table, our KATH algorithm employs a recursive indexing process that performs only r times of matrix indexing to generate all (r – 1)-depth K-ary trees, where the leaf node labels of a tree can uniquely specify the pattern. After that, the MinHash scheme is used to fingerprint the acquired subtree patterns for a graph. The experimental results show that our KATH algorithm runs significantly faster than state-of-the-art methods while achieving competitive or better accuracy. In Chapter 6, we propose a MinHash-based algorithm for network nodes, i.e., the NetHash algorithm. Consequently, each node in the network is mapped to a low-dimensional vector space. Our NetHash algorithm employs the randomized hashing technique to encode shallow trees, each of which is rooted at a node of the network. The main idea is to efficiently encode both attributes and structure information of each node by recursively sketching the corresponding rooted tree from bottom (i.e., highest-order neighboring nodes) to top (i.e., root node), and particularly, to preserve as much information closer to the root node as possible. The extensive experimental results show that the proposed algorithm, which does not need learning, runs significantly faster than the state-of-the-art learning-based network embedding methods while achieving competitive or even better performance in accuracy. Furthermore, the simplicity of the algorithm enables our NetHash algorithm to embed a millions-of-nodes network within 1,000 seconds on a single machine.
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