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Found 70 papers, 21 papers with code
GENE: Global Event Network Embedding
Current methods for event representation ignore related events in a corpus-level global context.
HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction
Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary.
EventKE: Event-Enhanced Knowledge Graph Embedding
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities.
Current and future directions in network biology
As such, it is expected to help shape short- and long-term vision for future computational and algorithmic research in network biology.
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning
To tackle both challenges together, in this paper, we propose a novel hyperedge prediction framework (CASH) that employs (1) context-aware node aggregation to precisely capture complex relations among nodes in each hyperedge for (C1) and (2) self-supervised contrastive learning in the context of hyperedge prediction to enhance hypergraph representations for (C2).
Ensuring User-side Fairness in Dynamic Recommender Systems
However, fairness-constrained re-ranking, a typical method to ensure user-side fairness (i. e., reducing performance disparity), faces two fundamental challenges in the dynamic setting: (1) non-differentiability of the ranking-based fairness constraint, which hinders the end-to-end training paradigm, and (2) time-inefficiency, which impedes quick adaptation to changes in user preferences.
Topological Augmentation for Class-Imbalanced Node Classification
Class imbalance is prevalent in real-world node classification tasks and often biases graph learning models toward majority classes.
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
We systematically review related works from the data to the computational aspects.
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues.
BeMap: Balanced Message Passing for Fair Graph Neural Network
Graph Neural Network (GNN) has shown strong empirical performance in many downstream tasks by iteratively aggregating information from the local neighborhood of each node, i. e., message passing.
Reconstructing Graph Diffusion History from a Single Snapshot
They are exclusively based on the maximum likelihood estimation (MLE) formulation and require to know true diffusion parameters.
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures.
Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning
A LERP is designed as a vector of probabilistic logical functions on the entity's neighboring sub-graph.
Ranked #9 on
Link Prediction
on WN18RR
Neural Multi-network Diffusion towards Social Recommendation
Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation.
FairGen: Towards Fair Graph Generation
In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability.
KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance.
Do We Really Need Complicated Model Architectures For Temporal Networks?
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning.
STERLING: Synergistic Representation Learning on Bipartite Graphs
Self-Supervised Learning (SSL) is a promising paradigm to address this challenge.
Concept Discovery for Fast Adapatation
The advances in deep learning have enabled machine learning methods to outperform human beings in various areas, but it remains a great challenge for a well-trained model to quickly adapt to a new task.
GENIUS: A Novel Solution for Subteam Replacement with Clustering-based Graph Neural Network
Subteam replacement is defined as finding the optimal candidate set of people who can best function as an unavailable subset of members (i. e., subteam) for certain reasons (e. g., conflicts of interests, employee churn), given a team of people embedded in a social network working on the same task.
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks
The proposed JuryGCN is capable of quantifying uncertainty deterministically without modifying the GCN architecture or introducing additional parameters.
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs
For general strongly observable graphs, we develop an algorithm that achieves the optimal regret $\widetilde{\mathcal{O}}((\sum_{t=1}^T\alpha_t)^{1/2}+\max_{t\in[T]}\alpha_t)$ with high probability, where $\alpha_t$ is the independence number of the feedback graph at round $t$.
Improved Algorithms for Neural Active Learning
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting.
Solving Coupled Differential Equation Groups Using PINO-CDE
This framework integrates engineering dynamics and deep learning technologies and may reveal a new concept for CDEs solving and uncertainty propagation.
Retrieval Based Time Series Forecasting
In this paper, we show both theoretically and empirically that the uncertainty could be effectively reduced by retrieving relevant time series as references.
ARIEL: Adversarial Graph Contrastive Learning
In this work, by introducing an adversarial graph view for data augmentation, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to extract informative contrastive samples within reasonable constraints.
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning
Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships.
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations.
Schema-Guided Event Graph Completion
Existing link prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph such as a social network or a knowledge graph, rather than multiple small dynamic event graphs.
CoNSoLe: Convex Neural Symbolic Learning
In the first step of searching for right symbols, we convexify the deep Q-learning.
COIN: Co-Cluster Infomax for Bipartite Graphs
We theoretically prove that COIN is able to effectively increase the mutual information of node embeddings and COIN is upper-bounded by the prior distributions of nodes.
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics.
Optimal Propagation for Graph Neural Networks
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input.
SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation
Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users.
Detecting Topology Attacks against Graph Neural Networks
In this work, we study the victim node detection problem under topology attacks against GNNs.
DISCO: Comprehensive and Explainable Disinformation Detection
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets.
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications.
Data Augmentation for Deep Graph Learning: A Survey
In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in different deep graph learning problems.
Adversarial Graph Contrastive Learning with Information Regularization
Contrastive learning is an effective unsupervised method in graph representation learning.
IMBENS: Ensemble Class-imbalanced Learning in Python
imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem.
Backdoor Attack through Frequency Domain
We also evaluate FTROJAN against state-of-the-art defenses as well as several adaptive defenses that are designed on the frequency domain.
Graph Communal Contrastive Learning
We argue that the community information should be considered to identify node pairs in the same communities, where the nodes insides are semantically similar.
Deep Active Learning by Leveraging Training Dynamics
Active learning theories and methods have been extensively studied in classical statistical learning settings.
Fair Regression under Sample Selection Bias
However, all previous fair regression research assumed the training data and testing data are drawn from the same distributions.
Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e. g., posts that represent user views) into an appropriate disentangled latent space.
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning
X-GOAL is comprised of two components: the GOAL framework, which learns node embeddings for each homogeneous graph layer, and an alignment regularization, which jointly models different layers by aligning layer-specific node embeddings.
Multiplex Graph Neural Network for Extractive Text Summarization
Extractive text summarization aims at extracting the most representative sentences from a given document as its summary.
Event Time Extraction and Propagation via Graph Attention Networks
Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.
InfoFair: Information-Theoretic Intersectional Fairness
The vast majority of the existing works on group fairness, with a few exceptions, primarily focus on debiasing with respect to a single sensitive attribute, despite the fact that the co-existence of multiple sensitive attributes (e. g., gender, race, marital status, etc.)
Hypergraph Pre-training with Graph Neural Networks
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting.
Graph Sanitation with Application to Node Classification
Generally speaking, the vast majority of the existing works aim to answer the following question, that is, given a graph, what is the best way to mine it?
Few-shot Network Anomaly Detection via Cross-network Meta-learning
Network anomaly detection aims to find network elements (e. g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority.
HDMI: High-order Deep Multiplex Infomax
To address the above-mentioned problems, we propose a novel framework, called High-order Deep Multiplex Infomax (HDMI), for learning node embedding on multiplex networks in a self-supervised way.
Network of Tensor Time Series
We propose a novel model called Network of Tensor Time Series, which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN).
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering
Collaborative filtering has been widely used in recommender systems.
KompaRe: A Knowledge Graph Comparative Reasoning System
In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs.
Fairness-aware Agnostic Federated Learning
In this paper, we develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution.
Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining
The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines.
Evaluating Graph Vulnerability and Robustness using TIGER
By democratizing the tools required to study network robustness, our goal is to assist researchers and practitioners in analyzing their own networks; and facilitate the development of new research in the field.
Robust Principal Component Analysis with Adaptive Neighbors
More significantly, the degree of the sparsity is steerable such that only exact k well-fitting samples with least reconstruction errors are activated during the optimization, while the residual samples, i. e., the extreme noised ones are eliminated for the global robustness.
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions.
EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data
A suite of visualizations is designed to illustrate the dynamics of city segmentation and the corresponding interactions are added to support the exploration of the segmentation patterns over time.
From Brain Imaging to Graph Analysis: a study on ADNI's patient cohort
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD).
Visual Analytics of Anomalous User Behaviors: A Survey
The increasing accessibility of data provides substantial opportunities for understanding user behaviors.
Panther: Fast Top-k Similarity Search in Large Networks
The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected.
Social and Information Networks
The Child is Father of the Man: Foresee the Success at the Early Stage
Understanding the dynamic mechanisms that drive the high-impact scientific work (e. g., research papers, patents) is a long-debated research topic and has many important implications, ranging from personal career development and recruitment search, to the jurisdiction of research resources.
Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations
In this paper, we propose a scalable, provable structured low-rank matrix factorization method to recover low-rank and sparse matrices from missing and grossly corrupted data, i. e., robust matrix completion (RMC) problems, or incomplete and grossly corrupted measurements, i. e., compressive principal component pursuit (CPCP) problems.
Graph-based Anomaly Detection and Description: A Survey
This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs.
Social and Information Networks Cryptography and Security
Want a Good Answer? Ask a Good Question First!
Community Question Answering (CQA) websites have become valuable repositories which host a massive volume of human knowledge.
GenDeR: A Generic Diversified Ranking Algorithm
In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples.
