Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

Social Network Analysis

We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.

https://dl.acm.org/doi/pdf/10.1145/3097983.3098036

metapath2vec: Scalable Representation Learning for Heterogeneous Networks

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

Probabilistic filters: A stream protocol for continuous probabilistic queries

The management of uncertainty in large databases has recently attracted tremendous research interest. Data uncertainty is inherent in many emerging and important applications, including locationbased services, wireless sensor networks, biometric and biological databases, and data stream applications. In these systems, it is important to manage data uncertainty carefully, in order to make correct decisions and provide high-quality services to users. To enable the development of these applications, uncertain database systems have been proposed. They consider data uncertainty as a "first-class citizen", and use generic data models to capture uncertainty, as well as provide query operators that return answers with statistical confidences.

We summarize our work on uncertain databases in recent years. We explain how data uncertainty can be modeled, and present a classification of probabilistic queries (e.g., range query and nearest-neighbor query). We further study how probabilistic queries can be efficiently evaluated and indexed. We also highlight the issue of removing uncertainty under a stringent cleaning budget, with an attempt of generating high-quality probabilistic answers.

Querying and cleaning uncertain data

Information in many applications, such as mobile wireless systems, social networks, and road networks, is captured by graphs, in many cases uncertain. We study the problem of querying a probabilistic graph; in particular, we examine “source-to-target” queries, such as computing the shortest path between two vertices. Evaluating ST-queries over probabilistic graphs is #P-hard, as it requires examining an exponential number of “possible worlds”. Existing solutions to the ST-query problem, which sample possible worlds, have two downsides: (i) many samples are needed for reasonable accuracy, and (ii) a possible world can be very large. To tackle these issues, we study the ProbTree, a data structure that stores a succinct representation of the probabilistic graph. Existing ST-query solutions are executed on top of this structure, with the number of samples and possible world sizes reduced.

/pdf/probtree-a-query-efficient-representation-of-probabilistic-2hajaaqq6s.pdf

ProbTree: a query-efficient representation of probabilistic graphs

Given a social network G, the Influence Maximization (IM) problem aims to find a seed set \(S \subseteq G\) of k users. These users are advertised, or activated, through marketing campaigns, with the hope that they will continue to influence others in G (e.g., by spreading messages about a new book). The goal of IM is to find the set S that achieves an optimal advertising effect or expected spread (e.g., make the largest number of users in G know about the book).

Maximizing Social Influence for the Awareness Threshold Model

Communities are prevalent in social networks, knowledge graphs, and biological networks. Recently, the topic of community search (CS), extracting a dense subgraph containing a query vertex q from a graph, has received great attention. However, existing CS solutions are designed for undirected graphs, and overlook directions of edges which potentially lose useful information carried on directions. In many applications (e.g., Twitter), users' relationships are often modeled as directed graphs (e.g., if a user a follows another user b, then there is an edge from a to b). In this paper, we study the problem of CS on directed graph. Given a vertex q of a graph G, we aim to find a densely connected subgraph containing q from G, in which vertices have strong interactions and high similarities, by using the minimum in/out-degrees metric. We first develop a baseline algorithm based on the concept of D-core. We further propose three index structures and corresponding query algorithms. Our experimental results on seven real graphs show that our solutions are very effective and efficient.

Reynold Cheng

Papers

Probabilistic filters: A stream protocol for continuous probabilistic queries

Querying and cleaning uncertain data

ProbTree: a query-efficient representation of probabilistic graphs

Maximizing Social Influence for the Awareness Threshold Model

Effective and Efficient Community Search Over Large Directed Graphs (Extended Abstract)