SNE: Signed Network Embedding

Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.Comment: To appear in PAKDD 201

COIN:Contrastive Identifier Network for Breast Mass Diagnosis in Mammography

Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement. In particular, data scarcity is attributed to the privacy and expensive annotation. And data entanglement is due to the high similarity between benign and malignant masses, of which manifolds reside in lower dimensional space with very small margin. To address these two challenges, we propose a deep learning framework, named Contrastive Identifier Network (\textsc{COIN}), which integrates adversarial augmentation and manifold-based contrastive learning. Firstly, we employ adversarial learning to create both on- and off-distribution mass contained ROIs. After that, we propose a novel contrastive loss with a built Signed graph. Finally, the neural network is optimized in a contrastive learning manner, with the purpose of improving the deep model's discriminativity on the extended dataset. In particular, by employing COIN, data samples from the same category are pulled close whereas those with different labels are pushed further in the deep latent space. Moreover, COIN outperforms the state-of-the-art related algorithms for solving breast cancer diagnosis problem by a considerable margin, achieving 93.4\% accuracy and 95.0\% AUC score. The code will release on ***

IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.Comment: 12 pages; appendix adde

Discovering structure without labels

The scarcity of labels combined with an abundance of data makes unsupervised learning more attractive than ever. Without annotations, inductive biases must guide the identification of the most salient structure in the data. This thesis contributes to two aspects of unsupervised learning: clustering and dimensionality reduction. The thesis falls into two parts. In the first part, we introduce Mod Shift, a clustering method for point data that uses a distance-based notion of attraction and repulsion to determine the number of clusters and the assignment of points to clusters. It iteratively moves points towards crisp clusters like Mean Shift but also has close ties to the Multicut problem via its loss function. As a result, it connects signed graph partitioning to clustering in Euclidean space. The second part treats dimensionality reduction and, in particular, the prominent neighbor embedding methods UMAP and t-SNE. We analyze the details of UMAP's implementation and find its actual loss function. It differs drastically from the one usually stated. This discrepancy allows us to explain some typical artifacts in UMAP plots, such as the dataset size-dependent tendency to produce overly crisp substructures. Contrary to existing belief, we find that UMAP's high-dimensional similarities are not critical to its success. Based on UMAP's actual loss, we describe its precise connection to the other state-of-the-art visualization method, t-SNE. The key insight is a new, exact relation between the contrastive loss functions negative sampling, employed by UMAP, and noise-contrastive estimation, which has been used to approximate t-SNE. As a result, we explain that UMAP embeddings appear more compact than t-SNE plots due to increased attraction between neighbors. Varying the attraction strength further, we obtain a spectrum of neighbor embedding methods, encompassing both UMAP- and t-SNE-like versions as special cases. Moving from more attraction to more repulsion shifts the focus of the embedding from continuous, global to more discrete and local structure of the data. Finally, we emphasize the link between contrastive neighbor embeddings and self-supervised contrastive learning. We show that different flavors of contrastive losses can work for both of them with few noise samples

End-to-End Quantum-like Language Models with Application to Question Answering

Language Modeling (LM) is a fundamental research topic ina range of areas. Recently, inspired by quantum theory, a novel Quantum Language Model (QLM) has been proposed for Information Retrieval (IR). In this paper, we aim to broaden the theoretical and practical basis of QLM. We develop a Neural Network based Quantum-like Language Model (NNQLM) and apply it to Question Answering. Specifically, based on word embeddings, we design a new density matrix, which represents a sentence (e.g., a question or an answer) and encodes a mixture of semantic subspaces. Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e.g., 2-dimensional convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have verified the effectiveness of our proposed models

Learning Robust Node Representations on Graphs

Graph neural networks (GNN), as a popular methodology for node representation learning on graphs, currently mainly focus on preserving the smoothness and identifiability of node representations. A robust node representation on graphs should further hold the stability property which means a node representation is resistant to slight perturbations on the input. In this paper, we introduce the stability of node representations in addition to the smoothness and identifiability, and develop a novel method called contrastive graph neural networks (CGNN) that learns robust node representations in an unsupervised manner. Specifically, CGNN maintains the stability and identifiability by a contrastive learning objective, while preserving the smoothness with existing GNN models. Furthermore, the proposed method is a generic framework that can be equipped with many other backbone models (e.g. GCN, GraphSage and GAT). Extensive experiments on four benchmarks under both transductive and inductive learning setups demonstrate the effectiveness of our method in comparison with recent supervised and unsupervised models.Comment: 16 page