
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Graph neural networks (GNNs) have achieved tremendous success on multipl...
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Neural Higherorder Pattern (Motif) Prediction in Temporal Networks
Dynamic systems that consist of a set of interacting elements can be abs...
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Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Selfsupervised learning of graph neural networks (GNN) is in great need...
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Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks
Hypergraph offers a framework to depict the multilateral relationships i...
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PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Classical recommender system methods typically face the filter bubble pr...
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Dual Attentive Sequential Learning for CrossDomain ClickThrough Rate Prediction
Cross domain recommender system constitutes a powerful method to tackle ...
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Dual Metric Learning for Effective and Efficient CrossDomain Recommendations
Cross domain recommender systems have been increasingly valuable for hel...
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MELOPPR: Software/Hardware Codesign for Memoryefficient Lowlatency Personalized PageRank
Personalized PageRank (PPR) is a graph algorithm that evaluates the impo...
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The Curse of Correlations for Robust Fingerprinting of Relational Databases
Database fingerprinting schemes have been widely adopted to prevent unau...
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Genomic Data Sharing under Dependent Local Differential Privacy
Privacypreserving genomic data sharing is prominent to increase the pac...
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Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
Temporal networks serve as abstractions of many realworld dynamic syste...
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Handling many conversions per click in modeling delayed feedback
Predicting the expected value or number of postclick conversions (purch...
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Revisiting graph neural networks and distance encoding in a practical view
Graph neural networks (GNNs) are widely used in the applications based o...
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FFADE: Frequency Factorization for Anomaly Detection in Edge Streams
Edge streams are commonly used to capture interactions in dynamic networ...
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Revisiting Graph Neural Networks for Link Prediction
Graph neural networks (GNNs) have achieved great success in recent years...
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Graph Information Bottleneck
Representation learning of graphstructured data is challenging because ...
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MStream: Fast Streaming MultiAspect Group Anomaly Detection
Given a stream of entries in a multiaspect data setting i.e., entries h...
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Distance Encoding – Design Provably More Powerful Graph Neural Networks for Structural Representation Learning
Learning structural representations of node sets from graphstructured d...
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Latent Unexpected Recommendations
Unexpected recommender system constitutes an important tool to tackle th...
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Joint Adaptive Feature Smoothing and Topology Extraction via Generalized PageRank GNNs
In many important applications, the acquired graphstructured data inclu...
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Landing Probabilities of Random Walks for SeedSet Expansion in Hypergraphs
We describe the first known meanfield study of landing probabilities fo...
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DDTCDR: Deep Dual Transfer Cross Domain Recommendation
Cross domain recommender systems have been increasingly valuable for hel...
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Towards Controllable and Personalized Review Generation
In this paper, we propose a novel model RevGAN that automatically genera...
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MetaGraph Based HIN Spectral Embedding: Methods, Analyses, and Insights
In this work, we propose to study the utility of different metagraphs, ...
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Subspace Determination through Local Intrinsic Dimensional Decomposition: Theory and Experimentation
Axisaligned subspace clustering generally entails searching through eno...
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Latent MultiCriteria Ratings for Recommendations
Multicriteria recommender systems have been increasingly valuable for h...
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Optimizing Generalized PageRank Methods for SeedExpansion Community Detection
Landing probabilities (LP) of random walks (RW) over graphs encode rich ...
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Latent Unexpected and Useful Recommendation
Providing unexpected recommendations is an important task for recommende...
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A tractable ellipsoidal approximation for voltage regulation problems
We present a machine learning approach to the solution of chance constra...
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Quadratic Decomposable Submodular Function Minimization: Theory and Practice
We introduce a new convex optimization problem, termed quadratic decompo...
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HS^2: Active Learning over Hypergraphs
We propose a hypergraphbased active learning scheme which we term HS^2,...
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Motif and Hypergraph Correlation Clustering
Motivated by applications in social and biological network analysis, we ...
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PersonJob Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning
PersonJob Fit is the process of matching the right talent for the right...
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Quadratic Decomposable Submodular Function Minimization
We introduce a new convex optimization problem, termed quadratic decompo...
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A CapacityPrice Game for Uncertain Renewables Resources
Renewable resources are starting to constitute a growing portion of the ...
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BEBP: An Poisoning Method Against Machine Learning Based IDSs
In big data era, machine learning is one of fundamental techniques in in...
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Revisiting Decomposable Submodular Function Minimization with Incidence Relations
We introduce a new approach to decomposable submodular function minimiza...
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Submodular Hypergraphs: pLaplacians, Cheeger Inequalities and Spectral Clustering
We introduce submodular hypergraphs, a family of hypergraphs that have d...
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Bayesian Renewables Scenario Generation via Deep Generative Networks
We present a method to generate renewable scenarios using Bayesian proba...
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Measuring the Popularity of Job Skills in Recruitment Market: A MultiCriteria Approach
To cope with the accelerating pace of technological changes, talents are...
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Inhomogeneous Hypergraph Clustering with Applications
Hypergraph partitioning is an important problem in machine learning, com...
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Distribution System Voltage Control under Uncertainties
Voltage control plays an important role in the operation of electricity ...
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Efficient Rank Aggregation via Lehmer Codes
We propose a novel rank aggregation method based on converting permutati...
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Multiclass MinMax Rank Aggregation
We introduce a new family of minmax rank aggregation problems under two ...
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An Optimal Treatment Assignment Strategy to Evaluate Demand Response Effect
Demand response is designed to motivate electricity customers to modify ...
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A Sparse Linear Model and Significance Test for Individual Consumption Prediction
Accurate prediction of user consumption is a key part not only in unders...
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