Combines curvature descriptors with TDA for graph model evaluation.
problem Evaluating graph generative models efficiently and accurately.
method Combines graph curvature descriptors with topological data analysis.
result Robust, expressive descriptors for graph generative models.
New framework assesses graph-learning datasets for better evaluation.
problem Insufficient evaluation of graph-learning datasets and methods.
method Introduces Rings framework for dataset ablations and proposes performance separability and mode complementarity measures.
result Demonstrates utility of Rings framework for graph-learning dataset evaluation.
New method uses contrastively trained GNNs for more reliable graph model evaluation.
problem Need effective methods to evaluate Graph Generative Models.
method Use representations from contrastively trained Graph Neural Networks (GNNs) for evaluation.
result Contrastively trained GNNs provide more reliable evaluation metrics than traditional or GNN-based approaches.
This paper evaluates metrics for graph generative models, addressing common pitfalls.
problem Evaluating and comparing graph generative models effectively.
method Systematic evaluation of MMD, analysis of synthetic and real graphs, practical recommendations.
result MMD can be problematic; practical solutions are provided.
New method evaluates financial graphs for stock trend forecasting.
problem Lack of dynamic stock relationship graphs and evaluation methods.
method SPNews dataset and novel evaluation methods independent of downstream tasks.
result Evaluation methods can differentiate between various financial relationship graphs.
Murakami-Ohtsuki-Yamada introduced an evaluation of certain oriented planar trivalent graphs with colored edges. This evaluation plays a key role in the evaluation of the colored HOMFLY polynomial of a link in 3-space and its Khovanov-Rozansky categorification. Our goal is is to give a generating series formula for the…
GraphBench creates a unified benchmark for graph learning tasks.
problem Fragmented benchmarking practices and inconsistent evaluation protocols in graph learning.
method Developed a comprehensive benchmark suite with standardized evaluation protocols.
result Established principled baselines for future research in graph learning.
TUDataset provides benchmark datasets for graph learning.
problem Lack of meaningful benchmark datasets and standardized evaluation procedures for graph learning.
method Collection and standardization of 120 graph datasets from various applications.
result Standardized evaluation procedures and baseline experiments provided.
PolyGraph Discrepancy improves graph generative model evaluation.
problem Inability of existing metrics to provide an absolute performance measure and comparability across different graph descriptors.
method Approximates Jensen-Shannon distance using binary classifiers trained to distinguish between real and generated graphs.
result PGD provides a more robust and insightful evaluation compared to MMD metrics.
OGB provides diverse graph datasets for robust ML research.
problem Challenges in scalable and robust graph machine learning.
method Unified evaluation protocol, diverse datasets, and automated pipeline.
result Significant scalability and generalization challenges identified.
This work benchmarks neural embeddings for link prediction in evolving knowledge graphs.
problem Evaluating the robustness of neural embeddings in changing knowledge graphs.
method Proposes an open-source evaluation pipeline using relation-centric connectivity measures.
result Demonstrates the importance of simulating embedding accuracy for frequently updated knowledge graphs.
We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these methods to a wider variety of problems. A thorough evaluation of these models is performed, and comparisons are made against established be…
A framework evaluates the impact of different modules in graph contrastive learning.
problem Insufficient module-level evaluation in existing graph contrastive learning methods.
method Proposes a framework decomposing GCL models into four modules for module-level evaluation.
result Identifies module-level guidelines and competitive performance of different modules.
This paper evaluates LLMs on large graph property estimation tasks.
problem Limited context length of LLMs limits their evaluation on large graphs.
method Developed EstGraph dataset and introduced four tasks for LLMs to estimate large graph properties.
result LLMs perform better on graph property estimation tasks when provided with context-rich prompts based on random walks.
We introduce a fully coherent spin network amplitude whose expansion generates all SU(2) spin networks associated with a given graph. We then give an explicit evaluation of this amplitude for an arbitrary graph. We show how this coherent amplitude can be obtained from the specialization of a generating functional obtai…
Study error bounds in evaluating distributional computational graphs.
problem Error analysis in evaluating graphs with inputs as probability distributions.
method Establish non-asymptotic error bounds using Wasserstein-1 distance.
result Non-asymptotic error bounds for discretization errors in distributional computational graphs.
New distances for causal graphs improve evaluation of learned structures.
problem Difficulty in evaluating graphs learned by causal discovery algorithms.
method Developed a framework for causal distances, including new reachability algorithms.
result Improved distances are faster and more scalable than existing methods.
This work evaluates graph models' robustness to structural distributional shifts.
problem Evaluating graph models' robustness to structural distributional shifts.
method Proposes a general approach for inducing diverse distributional shifts based on graph structure.
result Simple models often outperform more sophisticated methods on structural distributional shifts.
We focus our attention on the link prediction problem for knowledge graphs, which is treated herein as a binary classification task on neural embeddings of the entities. By comparing, combining and extending different methodologies for link prediction on graph-based data coming from different domains, we formalize a un…
With the rising interest in graph representation learning, a variety of approaches have been proposed to effectively capture a graph's properties. While these approaches have improved performance in graph machine learning tasks compared to traditional graph techniques, they are still perceived as techniques with limite…
GRETEL unifies GCE evaluation across various settings.
problem Lack of standardized evaluation for Graph Counterfactual Explanations.
method Unified framework for testing GCE methods in diverse settings.
result GRETEL promotes reproducible evaluations of GCE techniques.
In this paper, we define the curvature dimension inequalities CD(m, K) on finite directed graphs modifying the case of undirected graphs. As a main result, we evaluate m and K on finite directed graphs.
We establish a relation between the trace evaluation in SO(3) topological quantum field theory and evaluations of a topological Tutte polynomial. As an application, a generalization of the Tutte golden identity is proved for graphs on the torus.
The study evaluates memory and capacity of graph embedding methods.
problem Assessing the memory and capacity of graph embedding methods.
method Not specified in the abstract provided.
result Not specified in the abstract provided.
Study evaluates neural networks based on random graph structures and finds key performance indicators.
problem Understanding and optimizing neural network architectures using graph theory.
method Evaluation of neural networks with random graph structures, focusing on structural and numerical properties.
result A new numerical graph characteristic selects a set of quasi-1-dimensional graphs that perform well.
New relations for Penrose polynomial at n=4 and n=3.
problem Evaluation of Penrose polynomial at specific values of n.
method New ribbon graph polynomial and extended relations for n=3 to all n.
result Proven new relations for Penrose polynomial at n=4 and extended relations for n=3.
Network structure optimization is a fundamental task in complex network analysis. However, almost all the research on Bayesian optimization is aimed at optimizing the objective functions with vectorial inputs. In this work, we first present a flexible framework, denoted graph Bayesian optimization, to handle arbitrary …
FinReflectKG builds a comprehensive financial knowledge graph from SEC filings, improving extraction quality.
problem Lack of large-scale, open-source financial knowledge graph datasets.
method Intelligent document parsing, table-aware chunking, schema-guided iterative extraction, reflection-driven feedback loop.
result Reflection-agent-based mode achieves best balance of efficiency, accuracy, and reliability.
The paper generalizes virtual knot theory using multiple types of virtual crossings.
problem Generalizing virtual knot theory to include multiple types of virtual crossings.
method Starting with graph theory, the paper reviews previous work and then constructs multi-virtual knots and links.
result The multiplicity of virtual crossings allows for a broader application of the Penrose evaluation to all trivalent graphs.
We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings. Methods have been described previously for graph-to-graph autoencoding, but these approaches require sophisticated decoders that increase the complexity of training and evaluat…
This paper evaluates knowledge graph completion models under the open-world assumption, revealing unexpected behavior of metrics.
problem Evaluation of knowledge graph completion models often assumes a closed-world assumption, which can lead to misleading results.
method The paper studies KGC evaluation under the open-world assumption, analyzing the behavior of metrics like MRR and Hits@K.
result Metrics like MRR and Hits@K can show significant degradation under the open-world assumption, leading to incorrect model comparisons.
Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisfeiler-Leman test of graph isomorphism in terms of distinguishing non-isomorphic graphs. However, it was recently shown that this test cannot…
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs. It consists of state-of-the-art algorithms for defining embeddings of nodes whose connections evolve over time. The library also contains the evaluation framework for four downstream tasks on the network: graph reconstructi…
Survey of graph adversarial learning tasks and their attacks and defenses.
problem Uncertainty and unreliability of deep learning models on graphs against adversarial examples.
method Unified problem definition and comprehensive review of existing works.
result Unified definitions and taxonomies for graph adversarial learning tasks.
This work improves policy-based training by proposing an evaluation balance objective for GFlowNets.
problem Reliable estimation of policy divergence under directed acyclic graphs remains challenging.
method Proposes an evaluation balance objective over partial episodes to measure policy divergence and improve policy-based training reliability.
result Evaluation balance strengthens policy-based training reliability and broadens its flexibility.
BetaExplainer improves GNN interpretability by masking unimportant edges.
problem Interpreting GNNs' predictions is difficult due to black-box behavior and lack of uncertainty quantification.
method BetaExplainer uses a sparsity-inducing prior to mask unimportant edges during training.
result BetaExplainer provides uncertainty in edge importance and improves predictive accuracy on challenging datasets.
Survey on deep models for graph generation.
problem Improving fidelity of generated graphs.
method Taxonomy and comparison of deep generative models.
result Advances in deep generative models for graph generation.
In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically -- showing promising results. The following work investigates GNNs from a theoretical…
CogDL simplifies graph deep learning experiments and benchmarks.
problem Challenges in training and evaluating graph neural networks.
method Unified design for training and evaluation, mixed precision training, efficient sparse operators, ease of use.
result CogDL is the most competitive graph library for efficiency and ease of use.
New method models aptamer libraries as Boltzmann-weighted graph ensembles for better affinity predictions.
problem Anomalous candidates in SELEX datasets obscure true aptamer-ligand affinity.
method Boltzmann graph ensemble embeddings for thermodynamically parameterized exponential-family random graphs.
result Proposed embedding enables robust community detection and subgraph-level explanations for aptamer ligand affinity.
The study evaluates GRL approaches and finds limitations in their applicability.
problem Challenges in applying GRL approaches to real-world graphs with varying structural differences.
method Empirical data-driven framework and theoretical analysis of GRL approaches.
result Existing GRL approaches are insufficient for real-world graphs with diverse structural patterns.
Causal inference relies on the structure of a graph, often a directed acyclic graph (DAG). Different graphs may result in different causal inference statements and different intervention distributions. To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID). T…
Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on this task. Due to their massive success, GNNs have attracted a lot of attention, and many novel architectures have been put forward. In thi…
Despite the recent successes in robotic locomotion control, the design of robot relies heavily on human engineering. Automatic robot design has been a long studied subject, but the recent progress has been slowed due to the large combinatorial search space and the difficulty in evaluating the found candidates. To addre…
Large-scale graph data in real-world applications is often not static but dynamic, i. e., new nodes and edges appear over time. Current graph convolution approaches are promising, especially, when all the graph's nodes and edges are available during training. When unseen nodes and edges are inserted after training, it …
Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs. The validity of these algorithms tends to be evaluated by assessing the relationship between the learnt and the ground truth graph. However, there is no agreed scoring metric to determine this relationsh…
Authors provide a fair comparison of GNNs for graph classification.
problem Lack of reproducibility and rigorousness in experimental procedures for GNNs.
method Controlled and uniform framework with over 47,000 experiments.
result GNNs do not fully exploit structural information on some datasets.
PyKEEN 1.0 simplifies KGE model creation and optimization.
problem Training and evaluating knowledge graph embeddings (KGEs).
method Composes KGEMs with various interaction models, training approaches, and loss functions. Implements automatic memory optimization and extensive HPO functionalities.
result PyKEEN 1.0 streamlines KGE model creation and optimization.