Data-driven label space division outperforms random partitioning in multi-label classification.
problem Improving multi-label classification performance through better label space division.
method Community detection algorithms applied to label co-occurence graphs to partition the label space.
result Community detection methods on label co-occurence graphs outperform random partitioning in most evaluation measures.
Simple BoW model outperforms complex embeddings in knowledge graphs.
problem Improving knowledge graph embeddings for efficient training and performance.
method Used a Bag-of-Words (BoW) approach to model co-occurrences of entities and relations.
result Simple BoW model achieves state-of-the-art performance in knowledge graph tasks.
InfiniteWalk connects deep network embeddings to spectral graph theory with a nonlinear transformation.
problem Learning node representations from networks with deep learning methods.
method Study of the DeepWalk objective in the limit as window size goes to infinity, linking to spectral graph embeddings with a nonlinear transformation.
result Simple binary thresholding of the Laplacian pseudoinverse can approximate DeepWalk embeddings.
Improved image classification accuracy with a probabilistic model of label noise.
problem Noisy labels in large-scale image classification datasets.
method A probabilistic model using a multivariate Normal distribution on the final hidden layer of a neural network, capturing input-dependent label noise.
result Significantly improved accuracy on various datasets compared to standard methods.
Improved COD algorithm reduces streaming AMM errors and uses less space.
problem Efficiently approximate matrix multiplication with limited memory.
method Tighter error bound for COD, space optimality, sparse matrix variant.
result Improved COD is space optimal and more efficient for sparse matrices.
Continuous vector representations of words and objects appear to carry surprisingly rich semantic content. In this paper, we advance both the conceptual and theoretical understanding of word embeddings in three ways. First, we ground embeddings in semantic spaces studied in cognitive-psychometric literature and introdu…
Paper presents a multi-label topic model for financial texts with high performance and insights into market reactions.
problem Analyzing financial text data for market reactions and understanding topic interactions.
method Trained a multi-label topic model on a financial text database, achieved high macro F1 score, and investigated topic interactions.
result Model achieves high performance (macro F1 > 85%) and reveals significant market reactions to topic co-occurrences.
Bayesian model for dynamic sociability of network nodes.
problem Modeling sparse, time-varying networks with evolving node sociability.
method Dynamic point process model with time-varying generalised gamma process.
result Model yields sparse graphs with subquadratic edge growth.
Study quantifies interactions between unlabeled multimodal data.
problem Understanding how modalities combine in semi-supervised settings.
method Information-theoretic definitions and bounds derivation.
result Validated lower and upper bounds accurately track true interactions.
DDP models dynamic comorbidity networks from event data.
problem Understanding complex temporal patterns of co-occurring diseases.
method Developed deep diffusion processes (DDP) to model dynamic comorbidity networks.
result DDP enables accurate risk prediction and interpretable disease trajectories.
SGE learns symbolic node representations from relational data.
problem Mining insights from complex, real-world systems.
method SGE uses frequent pattern mining on a node's neighborhood to learn symbolic node representations.
result SGE outperforms shallow node embedding methods on a venue classification task.
Study reduces gender bias in web data used for image recognition.
problem Gender bias in web data amplifies in machine learning models.
method Inject corpus-level constraints for calibrating structured prediction models.
result Bias amplification decreased by 47.5% and 40.5% for multilabel classification and visual semantic role labeling.
Proposes a method to adapt labels on graphs with few labeled nodes.
problem Domain adaptation for graphs with limited labeled nodes.
method Optimization problem solving label transfer using spectral graph wavelets.
result Method yields satisfactory classification accuracy compared to existing methods.
edGNN improves graph embeddings for directed labeled graphs.
problem Improving node and graph embeddings for directed labeled graphs.
method edGNN is a GNN designed for directed labeled graphs, leveraging both topology and labels.
result edGNN is as powerful as the Weisfeiler-Lehman algorithm for graph isomorphism.
A new method improves node classification in graphs with limited labels.
problem Semi-supervised multi-label node classification in attributed graphs.
method Collaborative Graph Walk (Multi-Label-Graph-Walk) using reinforcement learning.
result Significantly better multi-label classification performance compared to state-of-the-art methods.
Local graph clustering improves with noisy labels, enhancing accuracy and performance.
problem Local graph clustering with noisy labels for node information.
method Constructing a weighted graph with noisy labels and using diffusion-based clustering.
result Diffusion in the weighted graph yields more accurate recovery of target clusters.
A new invariant for knotted graphs defined by label bracket.
problem Defining an invariant for knotted trivalent graphs.
method Generalizing Akimova and Manturov's construction to define the label bracket.
result The label bracket defines an isotopy invariant of knotted trivalent graphs.
A new method improves graph node embeddings by considering both nearby and distant node similarities.
problem Improving graph node embeddings by considering both nearby and distant node similarities.
method Distance-aware Negative Sampling (DNS) which maximizes cohesion at nearby node-pairs and separation at distant node-pairs.
result DNS outperforms baseline methods in downstream node classification tasks on various datasets and GRL algorithms.
New framework learns labels at both bag and graph levels.
problem Learning multi-label classifiers from multi-graph bags.
method Designing scoring functions and rank-loss objective for graph and bag levels; developing sub-gradient descent algorithm.
result Superior performance over state-of-the-art algorithms.
Proposes ML-GCN for multi-label graph node classification using GCN and relaxed skip-gram model.
problem Loss of label correlations in multi-label graph node classification.
method Uses a GCN to embed node features and graph topology, generates random label vectors, and detects correlations using a skip-gram model.
result Significantly outperforms state-of-the-art methods on graph classification datasets.
LGGAN generates labeled graphs from graph data.
problem Training generative models for graph-structured data with labels.
method LGGAN, a GAN approach, trains deep models for graph data with node labels.
result LGGAN generates diverse labeled graphs that match training data and outperforms alternatives.
Method transfers label function spectrum between graphs.
problem Domain adaptation with abrupt label function variations.
method Learning aligned graph bases to transfer label function spectrum.
result Improved classification performance compared to existing methods.
Constructs Lie algebras from labeled directed graphs and identifies properties of these algebras.
problem Constructing and analyzing Lie algebras from labeled directed graphs.
method Using labeled directed simple graphs to construct 2-step nilpotent Lie algebras, identifying ideals and subalgebras through special subgraphs, and proving isomorphisms based on label occurrences.
result Lie algebras depend only on the underlying undirected graph if all edges are labeled uniquely.
Graph filtering framework improves semi-supervised learning efficiency.
problem Efficiently leverage unlabeled data with limited labeled data.
method Graph filtering to inject graph similarity into data features.
result Unified insights and improved modeling capabilities of label propagation and graph convolutional networks.
Proposes ML-GCN for multi-label network node representation learning.
problem Complex multi-label networks with correlated labels.
method Two Siamese GCNs model node-label and label-label interactions, integrated under a unified objective function.
result Effective node representation learning with preserved label interactions.
GAML tackles multilabel classification over graphs using message passing and attention.
problem Multilabel classification over graphs with variable-size substructures and label-substructure relations.
method GAML uses a graph neural network that models labels as auxiliary nodes and iteratively applies message passing and attention mechanisms.
result GAML significantly outperforms other methods and provides intuitive visualizations.
Proposes methods to recover labels from shuffled networks using graph averages.
problem Recovering labels from a shuffled network using graph averages.
method Cluster networks into classes, then match the new graph to cluster-averages, minimizing the graph matching objective function.
result Higher fidelity matching performance when clustering networks into different classes.
Improved graph classification with noisy labels using GNNs and loss correction.
problem Robustness of GNNs to symmetric label noise.
method Combining GNNs with loss correction methods.
result Test accuracy improvement under noisy conditions.
Graph ConvNet improves classification by leveraging label graph structure.
problem Ignoring label graph structure in multi-class classification leads to suboptimal performance.
method Proposes a GCN-based neural network classifier that incorporates the graph structure of labels.
result The proposed model outperforms baseline methods in terms of graph-theoretic metrics.
KGNN-LS improves recommender systems using knowledge graphs and label smoothness.
problem Improving recommender systems through better user-item embeddings.
method KGNN-LS combines knowledge graphs, user-specific embeddings, and label smoothness regularization.
result KGNN-LS outperforms state-of-the-art baselines and handles cold-start scenarios.
AUC-spec optimizes graph-based SSL for complex label distributions.
problem Training accurate models with scarce labeled data and abundant unlabeled data.
method Computes a low-dimensional representation that maximizes class separation via AUC optimization.
result AUC-spec achieves competitive results on synthetic and real-world datasets.
Paper proposes M3S training for GCNs on graphs with few labels.
problem Learning graph embeddings with few labeled nodes is challenging.
method Multi-Stage Self-Supervised (M3S) Training Algorithm combining self-supervised learning.
result M3S Training Algorithm improves GCNs' generalization on graphs with few labeled nodes.
HopGAT improves node classification in sparsely labeled graphs by learning from distant neighbors.
problem Classifying nodes in sparsely labeled graphs with limited labeled data.
method Hop-aware supervision mechanism and simulated annealing learning strategy.
result The model achieves high accuracy even with 40% labeled data, reducing performance loss to 3.9%.
Unified model combines feature and label propagation for semi-supervised classification.
problem Combining feature and label propagation for effective semi-supervised classification.
method Unified Message Passing Model (UniMP) using Graph Transformer and masked label prediction.
result Obtains new state-of-the-art results in Open Graph Benchmark (OGB).
New algorithm reduces matrix multiplication time for sparse matrices.
problem Efficiently multiply large sparse matrices with limited space.
method Exploits sparsity to reduce QR decompositions and time complexity.
result Time complexity reduced to $\widetilde{O}\left((
nz(X)+
nz(Y))\ell+n\ell^2
ight)$ in expectation.
A {\em word labeled oriented graph} (WLOG) is an oriented graph G on vertices X={x1,…,xk}, where each oriented edge is labeled by a word in X±1. WLOGs give rise to presentations which generalize Wirtinger presentations of knots. WLOG presentations, where the underlying graph is a tree are of …
New method estimates graph compatibility from sparse labels.
problem Estimating graph compatibility from sparse labeled data.
method Factorized graph representations and algebraic amplification.
result End-to-end classification accuracy comparable to gold standard.
Study abelian factors in Lie algebras from graph edge labels.
problem Understanding abelian factors in Lie algebras from graph edge labels.
method Analyzing 2-step nilpotent Lie algebras constructed from graphs, computing abelian factors, and studying singularity properties.
result Explicit computation of abelian factors for various graph families.
Bayesian SSR on graphs improves regression with noisy labels.
problem Estimating function values on graphs from noisy labeled data.
method Bayesian approach using graph Laplacian and Gaussian prior.
result Rates of contraction of posterior measure around ground truth.
Bayesian method predicts labels on large graphs using Laplacian eigenfunctions.
problem Binary classification on large graphs.
method Hierarchical Bayesian approach with truncated Laplacian regularization.
result Improved scalability for large graphs compared to untruncated Laplacian.
BeGIN benchmarks GNNs for instance-dependent label noise in graphs.
problem Instance-dependent label noise in graph data.
method BeGIN introduces a benchmark with various noise types and evaluates noise-handling strategies across GNN architectures.
result Challenges of instance-dependent noise, especially LLM-based corruption, and the importance of node-specific parameterization.
Can we identify node labels from graph labels?
problem Identifying node labels from graph labels in a hierarchical network.
method Gaussian Mixture Graph Convolutional Network (GMGCN) with Graph Attention Network (GAT) and Gaussian Mixture Layer (GML).
result The proposed method outperforms other baselines on various benchmarks.
Algorithm learns graph topologies to adapt labels between source and target graphs.
problem Domain adaptation on graphs with different data manifolds.
method Proposes a graph domain adaptation algorithm that learns both graph topologies and label functions.
result Improves classification performance as graph topologies become more balanced.
GRAPE uses graph representation to handle missing data in feature imputation and label prediction.
problem Handling missing data in machine learning tasks.
method GRAPE uses a bipartite graph where observations and features are nodes, and observed feature values are edges. It formulates feature imputation as edge-level prediction and label prediction as node-level prediction, solving these with Graph Neural Networks.
result GRAPE achieves 20% lower mean absolute error for imputation and 10% lower for label prediction compared to state-of-the-art methods.
Paper tackles label insufficiency and inaccuracy in semi-supervised learning.
problem Label insufficiency and inaccuracy in semi-supervised learning.
method Graph-based propagation for label insufficiency and label filtering for inaccuracy.
result SIIS improves performance in the presence of label noise and scarcity.
This paper defines a duality for labeled graphs and factorizations, linking them to graph embeddings and Hurwitz enumeration.
problem Understanding the relationship between labeled graphs and factorizations in symmetric groups.
method Defining a mind-body duality and interpreting it in terms of Properly Embedded Graphs and Cellularly Embedded Graphs.
result Established a connection between factorizations, labeled graphs, and graph embeddings, including applications to Hurwitz enumeration.
LC-GNN improves GNNs for node classification by incorporating label consistency.
problem Limited performance of GNNs due to label consistency assumption not always holding.
method LC-GNN uses node pairs with the same label but unconnected to expand GNN's receptive field.
result LC-GNN outperforms traditional GNNs in semi-supervised node classification.
Graph-based multi-label classifier extends CULP for multi-label data.
problem Solving multi-label classification problems.
method Extends CULP algorithm to handle multi-label data.
result Competitive results compared to cutting-edge multi-label classifiers.