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Found 19 papers, 15 papers with code
GraphWiz: An Instruction-Following Language Model for Graph Problems
Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored.
GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a standard comprehensive setting, (2) whether GNNs can outperform traditional algorithms such as tree ensembles, and (3) how about their efficiency on large-scale graphs.
A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment
In this paper, we introduce FGWEA, an unsupervised entity alignment framework that leverages the Fused Gromov-Wasserstein (FGW) distance, allowing for a comprehensive comparison of entity semantics and KG structures within a joint optimization framework.
Missing Data Imputation with Graph Laplacian Pyramid Network
Data imputation is a prevalent and important task due to the ubiquitousness of missing data.
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
This observation allows us to provide an approximation bound for the distance between the fixed-point set of BAPG and the critical point set of GW.
Outlier-Robust Gromov-Wasserstein for Graph Data
Gromov-Wasserstein (GW) distance is a powerful tool for comparing and aligning probability distributions supported on different metric spaces.
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
As the graphs to be aligned are usually constructed from different sources, the inconsistency issues of structures and features between two graphs are ubiquitous in real-world applications.
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS.
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy.
Rethinking Graph Neural Networks for Anomaly Detection
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection.
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data
In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks.
"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics
In this paper, we explore how to bring interpretability to data-driven DSMD.
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks
Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasonable predictions.
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning
Therefore, we propose a Geometric Question Answering dataset GeoQA, containing 4, 998 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems.
Ranked #4 on
Mathematical Reasoning
on PGPS9K
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
Besides, we develop a Graph-Evolving Meta-Learning (GEML) framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease, which effectively alleviates the needs of a large number of dialogues.
MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation
To push forward the future research on building expert-sensitive medical dialogue system, we proposes two kinds of medical dialogue tasks based on MedDG dataset.
Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation
Target-guided open-domain conversation aims to proactively and naturally guide a dialogue agent or human to achieve specific goals, topics or keywords during open-ended conversations.
Target-Guided Open-Domain Conversation
We study the problem of imposing conversational goals on open-domain chat agents.
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
Besides the challenges for conversational dialogue systems (e. g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations.
