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Found 93 papers, 40 papers with code
GOT4Rec: Graph of Thoughts for Sequential Recommendation
With the advancement of large language models (LLMs), researchers have explored various methods to optimally leverage their comprehension and generation capabilities in sequential recommendation scenarios.
Playing Language Game with LLMs Leads to Jailbreaking
In this paper, we introduce two novel jailbreak methods based on mismatched generalization: natural language games and custom language games, both of which effectively bypass the safety mechanisms of LLMs, with various kinds and different variants, making them hard to defend and leading to high attack rates.
Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
We attribute this issue to the imbalance between the abundance of tunable parameters and the scarcity of labeled molecules, and the lack of contextual perceptiveness in the encoders.
Bi-Level Graph Structure Learning for Next POI Recommendation
To address the aforementioned issues, this paper presents a novel Bi-level Graph Structure Learning (BiGSL) for next POI recommendation.
Beyond Filtering: Adaptive Image-Text Quality Enhancement for MLLM Pretraining
A critical factor in training MLLMs is the quality of image-text pairs within multimodal pretraining datasets.
Uncovering Overfitting in Large Language Model Editing
Knowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs).
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization
Therefore, we propose a Molecular data Pruning framework for enhanced Generalization (MolPeg), which focuses on the source-free data pruning scenario, where data pruning is applied with pretrained models.
Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing
Building on the validated hypothesis, we propose a novel knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE).
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization
This paper addresses the challenge of out-of-distribution (OOD) generalization in graph machine learning, a field rapidly advancing yet grappling with the discrepancy between source and target data distributions.
Modality-Balanced Learning for Multimedia Recommendation
To address these issues, we propose a Counterfactual Knowledge Distillation method that could solve the imbalance problem and make the best use of all modalities.
Navigating the Noisy Crowd: Finding Key Information for Claim Verification
These keywords serve as a guide to extract and summarize critical information into abstracted evidence.
Learning Domain-Invariant Features for Out-of-Context News Detection
In order to effectively adapt the detection model to unlabeled news topics or agencies, we propose ConDA-TTA (Contrastive Domain Adaptation with Test-Time Adaptation) which applies contrastive learning and maximum mean discrepancy (MMD) to learn domain-invariant features.
Interpretable Multimodal Out-of-context Detection with Soft Logic Regularization
By employing latent variables for phrase-level predictions, the final prediction of the image-caption pair can be aggregated using logical rules.
Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection
The model learns semantic evolvement information of events by capturing local semantic changes and global semantic evolvement information through specific graph autoencoder and reconstruction strategies.
Out-of-distribution Rumor Detection via Test-Time Adaptation
This method models the propagation of news in the form of a propagation graph, and builds propagation graph test-time adaptation framework, enhancing the model's adaptability and robustness when facing OOD problems.
VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark
Compared to this, editing Large Vision-Language Models (LVLMs) faces extra challenges from diverse data modalities and complicated model components, and data for LVLMs editing are limited.
Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media
One of the unique challenges for fake news detection on social media is how to detect fake news on future events.
Chain-of-History Reasoning for Temporal Knowledge Graph Forecasting
Temporal Knowledge Graph (TKG) forecasting aims to predict future facts based on given histories.
Knowledge Graph Enhanced Large Language Model Editing
Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks, yet their efficacy is hampered by inaccuracies and outdated knowledge.
Text-Guided Molecule Generation with Diffusion Language Model
In this work, we propose the Text-Guided Molecule Generation with Diffusion Language Model (TGM-DLM), a novel approach that leverages diffusion models to address the limitations of autoregressive methods.
Ranked #8 on
Text-based de novo Molecule Generation
on ChEBI-20
Language Modelling
Text-based de novo Molecule Generation
+1
Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables
To address this, we propose a novel word-level Heterogeneous-graph-based model for Fact Checking over unstructured and structured information, namely HeterFC.
Stealthy Attack on Large Language Model based Recommendation
Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS).
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
In this work, we adopt the intuition that the LVLM tends to respond logically consistently for existent objects but inconsistently for hallucinated objects.
Rethinking Graph Masked Autoencoders through Alignment and Uniformity
Based on our theoretical analysis, we further identify the limitations of the GraphMAE from the perspectives of alignment and uniformity, which have been considered as two key properties of high-quality representations in GCL.
Can Large Language Models Detect Rumors on Social Media?
In this work, we investigate to use Large Language Models (LLMs) for rumor detection on social media.
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification
Each instance is accompanied by a veracity label and an explanation that outlines the reasoning path supporting the veracity classification.
GSLB: The Graph Structure Learning Benchmark
To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms.
Uncovering Neural Scaling Laws in Molecular Representation Learning
Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design.
TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis
Motivated by the related medical findings on functional connectivites, TiBGL proposes template-induced brain graph learning to extract template brain graphs for all groups.
CvFormer: Cross-view transFormers with Pre-training for fMRI Analysis of Human Brain
In recent years, functional magnetic resonance imaging (fMRI) has been widely utilized to diagnose neurological disease, by exploiting the region of interest (RoI) nodes as well as their connectivities in human brain.
Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation
Even worse, the strong statistical correlation might mislead models to learn the spurious preference towards inconsequential modalities.
Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing
Then, DAL reversely optimizes news-aspect and evidence-aspect debiasing discriminators to mitigate the impact of news and evidence content biases.
Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation
However, none of existing multi-interest recommendation models consider the Out-Of-Distribution (OOD) generalization problem, in which interest distribution may change.
MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning
Most existing works focus on exploring evolutionary information in history to obtain effective temporal embeddings for entities and relations, but they ignore the variation in evolution patterns of facts, which makes them struggle to adapt to future data with different evolution patterns.
The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection
In this paper, we argue that the performance degradation is mainly attributed to the inconsistency between topology and attribute.
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
Comprehensive experiments have demonstrated the superiority of GETRAL over the state-of-the-arts and validated the efficacy of semantic mining with graph structure and contrastive learning.
Improving Molecular Pretraining with Complementary Featurizations
Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery.
Second-Order Global Attention Networks for Graph Classification and Regression
Graph Neural Networks (GNNs) are powerful to learn representation of graph-structured data, which fuse both attributive and topological information.
RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring
Accordingly, we for the first time propose to model the biased credit scoring data with Multi-Task Learning (MTL).
A Survey on Deep Graph Generation: Methods and Applications
In this paper, we conduct a comprehensive review on the existing literature of deep graph generation from a variety of emerging methods to its wide application areas.
Evidence-aware Fake News Detection with Graph Neural Networks
In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i. e., a claim).
AI in Human-computer Gaming: Techniques, Challenges and Opportunities
Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer gaming; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs.
Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships. To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity).
Relation-aware Heterogeneous Graph for User Profiling
User profiling has long been an important problem that investigates user interests in many real applications.
An Empirical Study of Graph Contrastive Learning
We envision this work to provide useful empirical evidence of effective GCL algorithms and offer several insights for future research.
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data.
Deep Contrastive Multiview Network Embedding
In our work, different views can be obtained based on the various relations among nodes.
Deep Active Learning for Text Classification with Diverse Interpretations
To train high-performing models with the minimal annotation cost, active learning is proposed to select and label the most informative samples, yet it is still challenging to measure informativeness of samples used in DNNs.
Fully Hyperbolic Graph Convolution Network for Recommendation
These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs.
Label-informed Graph Structure Learning for Node Classification
Graph Neural Networks (GNNs) have achieved great success among various domains.
GraphFM: Graph Factorization Machines for Feature Interaction Modeling
To solve the problems, we propose a novel approach, Graph Factorization Machine (GraphFM), by naturally representing features in the graph structure.
Explainable Enterprise Credit Rating via Deep Feature Crossing Network
Experimental results obtained on real-world enterprise datasets verify that the proposed approach achieves higher performance than conventional methods, and provides insights into individual rating results and the reliability of model training.
Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation
Our proposed Co-occurrence based Enhanced Representation model (CER) learns the scoring function by a deep neural network with the attentive user representation and fusion of raw representation and enhanced representation of target item as input.
Mining Latent Structures for Multimedia Recommendation
To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs.
Dynamic Graph Neural Networks for Sequential Recommendation
We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information.
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
We naturally generalizes the embedding propagation scheme of GCN to dynamic setting in an efficient manner, which is to propagate the change along the graph to update node embeddings.
Graph Classification by Mixture of Diverse Experts
Graph classification is a challenging research problem in many applications across a broad range of domains.
GraphDIVE: Graph Classification by Mixture of Diverse Experts
Graph classification is a challenging research problem in many applications across a broad range of domains.
A Survey on Graph Structure Learning: Progress and Opportunities
Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains.
Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval
In addition, due to the complexity and scale of the document collections, it is considerable to explore the different grain-sized hierarchical matching signals at a more general level.
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial.
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
To better model complex feature interaction, in this paper we propose a novel DisentanglEd Self-atTentIve NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the computation of unary feature importance from pairwise interaction.
Dynamic Graph Collaborative Filtering
Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time.
Cold-start Sequential Recommendation via Meta Learner
As there is generally no side information in the setting of sequential recommendation task, previous cold-start methods could not be applied when only user-item interactions are available.
Heterogeneous Graph Collaborative Filtering
In this work, we propose to model user-item interactions as a heterogeneous graph which consists of not only user-item edges indicating their interaction but also user-user edges indicating their similarity.
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision
To this end, we present a minimax selection scheme that explicitly harnesses neighborhood information and discover homophilous subgraphs to facilitate active selection.
Graph Contrastive Learning with Adaptive Augmentation
On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information.
DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
These item transitions include potential collaborative information and reflect similar behavior patterns, which we assume may help with the recommendation for the target session.
Ranked #6 on
Session-Based Recommendations
on Diginetica
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
In CAGNN, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments.
One Shot 3D Photography
3D photos are static in time, like traditional photos, but are displayed with interactive parallax on mobile or desktop screens, as well as on Virtual Reality devices, where viewing it also includes stereo.
Disentangled Item Representation for Recommender Systems
In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation.
TFNet: Multi-Semantic Feature Interaction for CTR Prediction
The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems.
Ranked #33 on
Click-Through Rate Prediction
on Criteo
Deep Graph Contrastive Representation Learning
Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.
Ranked #1 on
Node Classification
on DBLP
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
However, these methods compress a session into one fixed representation vector without considering the target items to be predicted.
Ranked #3 on
Session-Based Recommendations
on yoochoose1
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document.
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions
IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields.
Ranked #2 on
Click-Through Rate Prediction
on KKBox
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions
IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields.
Independence Promoted Graph Disentangled Networks
In this paper, we propose a novel Independence Promoted Graph Disentangled Networks (IPGDN) to learn disentangled node representation while enhancing the independence among node representations.
Learning Preferences and Demands in Visual Recommendation
For modeling users' demands on different categories of items, the problem can be formulated as recommendation with contextual and sequential information.
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Then, in order to better capture the complicated non-linearity of graph data, we present a novel GraphAIR framework which models the neighborhood interaction in addition to neighborhood aggregation.
Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation
The other is Dot-Product Attention mechanism, which draws on the Transformer net to explicitly model the effect of historical sessions on the current session.
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
The key of this task is to model feature interactions among different feature fields.
Ranked #10 on
Click-Through Rate Prediction
on Avazu
Semi-supervised Compatibility Learning Across Categories for Clothing Matching
To achieve the alignment, we minimize the distances between distributions with unsupervised adversarial learning, and also the distances between some annotated compatible items which play the role of anchor points to help align.
Learning Vertex Convolutional Networks for Graph Classification
In this paper, we develop a new aligned vertex convolutional network model to learn multi-scale local-level vertex features for graph classification.
Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks
In this paper, we aim to investigate a practical problem of fashion recommendation by answering the question "which item should we select to match with the given fashion items and form a compatible outfit".
Ranked #1 on
Recommendation Systems
on Polyvore
(Accuracy metric)
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining.
Session-based Recommendation with Graph Neural Networks
To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i. e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity.
Ranked #1 on
Session-Based Recommendations
on Gowalla
A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation
We fuse the current hidden state and a contextual hidden state built by the attention mechanism, which leads to a more suitable user's overall interest.
ICE: Information Credibility Evaluation on Social Media via Representation Learning
In this paper, we propose a novel representation learning method, Information Credibility Evaluation (ICE), to learn representations of information credibility on social media.
Context-aware Sequential Recommendation
Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks.
A Comprehensive Survey on Cross-modal Retrieval
To speed up the cross-modal retrieval, a number of binary representation learning methods are proposed to map different modalities of data into a common Hamming space.
A Convolutional Click Prediction Model
The explosion in online advertisement urges to better estimate the click prediction of ads.
A Convolutional Click Prediction Model
The explosion in online advertisement urges to better estimate the click prediction of ads.
