Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Social Network Analysis

Background: Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy and politics. This review focuses specifically on applications related to health, which is defined as 'a state of complete physical, mental and social wellbeing and not merely the absence of disease or infirmity'. Objective: The main aim of this study is to establish the state of the art in SA related to health and wellbeing by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and wellbeing are affected, we focus specifically on spontaneously generated content and not necessarily that of healthcare professionals. Methods: Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multi-faceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used and their evaluation. Results: The majority of data was collected from social networking and online retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians and healthcare services in general. We identified five roles with respect to health and wellbeing among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer and a suicide victim. Out of 86 studies considered, only four reported the demographics characteristics. A wide range of methods have been used to perform SA. Most common choices include support vector machines, naive Bayesian learning, decision trees, logistic regression and adaptive boosting. In contrast to general trends in SA research, only one study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and wellbeing was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes. Conclusions: SA results in the area of health and wellbeing lad behind those in other domains. It is yet unclear if this is due to the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica or the choice of algorithms.

Sentiment Analysis in Health and Well-Being: Systematic Review.

Motivation The emergence of abundant biological networks, which benefit from the development of advanced high-throughput techniques, contributes to describing and modeling complex internal interactions among biological entities such as genes and proteins. Multiple networks provide rich information for inferring the function of genes or proteins. To extract functional patterns of genes based on multiple heterogeneous networks, network embedding-based methods, aiming to capture non-linear and low-dimensional feature representation based on network biology, have recently achieved remarkable performance in gene function prediction. However, existing methods do not consider the shared information among different networks during the feature learning process. Results Taking the correlation among the networks into account, we design a novel semi-supervised autoencoder method to integrate multiple networks and generate a low-dimensional feature representation. Then we utilize a convolutional neural network based on the integrated feature embedding to annotate unlabeled gene functions. We test our method on both yeast and human datasets and compare with three state-of-the-art methods. The results demonstrate the superior performance of our method. We not only provide a comprehensive analysis of the performance of the newly proposed algorithm but also provide a tool for extracting features of genes based on multiple networks, which can be used in the downstream machine learning task. Availability DeepMNE-CNN is freely available at https://github.com/xuehansheng/DeepMNE-CNN. Contact jiajiepeng@nwpu.edu.cn; shang@nwpu.edu.cn; jianye.hao@tju.edu.cn.

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Integrating multi-network topology for gene function prediction using deep neural networks

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

Introduction to Graph Neural Networks

Real-world social networks and digital platforms are comprised of individuals (nodes) that are linked to other individuals or entities through multiple types of relationships (links). Sub-networks of such a network based on each type of link correspond to distinct views of the underlying network. In real-world applications each node is typically linked to only a small subset of other nodes. Hence, practical approaches to problems such as node labeling have to cope with the resulting sparse networks. While low-dimensional network embeddings offer a promising approach to this problem, most of the current network embedding methods focus primarily on single view networks. We introduce a novel multi-view network embedding (MVNE) algorithm for constructing low-dimensional node embeddings from multi-view networks. MVNE adapts and extends an approach to single view node embedding using graph factorization clustering (GFC) to the multi-view setting using an objective function that maximizes the agreement between views based on both the local and global structure of the underlying multi-view graph. Our experiments with several benchmark real-world single view networks show that SVNE yields network embeddings that are competitive with or superior to those produced by the state-of-the-art single view network embedding methods when the embeddings are used for labeling unlabeled nodes in the networks. Our experiments with several multi-view networks show that MVNE substantially outperforms the single view methods on integrated view and the state-of-the-art multi-view methods. We further show that even when the goal is to predict labels of nodes within a single target view, MVNE outperforms its single-view counterpart suggesting that the MVNE is able to extract the information that is useful for labeling nodes in the target view from the all of the views.

Multi-view Network Embedding via Graph Factorization Clustering and Co-regularized Multi-view Agreement

Online health communities (OHCs) constitute a useful source of information and social support for patients. American Cancer Society’s Cancer Survivor Network (CSN), a 173 000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. Discussion threads are multiparty conversations that often provide a source of social support, e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that the members of an OHC derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. We introduce a novel framework to examine the temporal causality of sentiment dynamics in the CSN. We construct a probabilistic computation tree logic representation and a corresponding probabilistic Kripke structure to represent and reason about the changes in sentiments of posts in a thread over time. We use a sentiment classifier trained using machine learning on a set of posts manually tagged with sentiment labels to classify posts as expressing either positive or negative sentiment. We analyze the probabilistic Kripke structure to identify the prima facie causes of sentiment change on the part of the thread originators in the CSN forum and their significance. We find that the sentiment of replies appears to causally influence the sentiment of the thread originator. Our experiments also show that the conclusions are robust with respect to the choice of the: 1) classification threshold of the sentiment classifier and 2) choice of the specific sentiment classifier used. We also extend the basic framework for temporal causality analysis to incorporate the uncertainty in the states of the probabilistic Kripke structure resulting from the use of an imperfect state transducer (in our case, the sentiment classifier). Our analysis of temporal causality of CSN sentiment dynamics offers new insights that the designers, managers, and moderators of an online community, such as CSN, can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the CSN participants. The proposed methodology for the analysis of temporal causality has broad applicability in a variety of settings where the dynamics of the underlying system can be modeled in terms of state variables that change in response to internal or external inputs.

Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network

Online health communities (OHCs) constitute a useful source of information and social support for patients. American Cancer Society’s Cancer Survivor Network (CSN), a 173,000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. It captures a multi-party conversation that often serves the function of providing social support e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that members of OHC derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. This paper reports results of a study that seeks to address this gap by discovering temporal causality of the dynamics of sentiment change (on the part of the thread originators) in CSN. The resulting accounts offer new insights that the designers, managers and moderators of an online community such as CSN can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the community participants. The proposed methodology also has broad applications in the discovery of temporal causality from big data.

/pdf/temporal-causality-of-social-support-in-an-online-community-4qtyeqspo6.pdf

Temporal Causality of Social Support in an Online Community for Cancer Survivors

Real world social networks typically consist of actors (individuals) that are linked to other actors or different types of objects via links of multiple types. Different types of relationships induce different views of the underlying social network. We consider the problem of labeling actors in such multi-view networks based on the connections among them. Given a social network in which only a subset of the actors are labeled, our goal is to predict the labels of the rest of the actors. We introduce a new random walk kernel, namely the Inter-Graph Random Walk Kernel (IRWK), for labeling actors in multi-view social networks. IRWK combines information from within each of the views as well as the links across different views. The results of our experiments on two real-world multi-view social networks show that: (i) IRWK classifiers outperform or are competitive with several state-of-the-art methods for labeling actors in a social network; (ii) IRWKs are robust with respect to different choices of user-specified parameters; and (iii) IRWK kernel computation converges very fast within a few iterations.

Labeling actors in multi-view social networks by integrating information from within and across multiple views

Many big data applications give rise to distributional data wherein objects or individuals are naturally represented as K-tuples of bags of feature values where feature values in each bag are sampled from a feature and object specific distribution. We formulate and solve the problem of learning classifiers from distributional data. We consider three classes of methods for learning distributional classifiers: (i) those that rely on aggregation to encode distributional data into tuples of attribute values, i.e., instances that can be handled by traditional supervised machine learning algorithms, (ii) those that are based on generative models of distributional data, and (iii) the discriminative counterparts of the generative models considered in (ii) above. We compare the performance of the different algorithms on real-world as well as synthetic distributional data sets. The results of our experiments demonstrate that classifiers that take advantage of the information available in the distributional instance representation outperform or match the performance of those that fail to fully exploit such information.

Ngot Bui

Papers

Multi-view Network Embedding via Graph Factorization Clustering and Co-regularized Multi-view Agreement

Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network

Temporal Causality of Social Support in an Online Community for Cancer Survivors

Labeling actors in multi-view social networks by integrating information from within and across multiple views

Learning Classifiers from Distributional Data