Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs. By replacing regular convolutions with linear shift-invariant graph filters (LSI-GFs), GNNs take into account the (irregular) structure of the graph and provide meaning…
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48 results for “activation graphs”
Paper tackles active learning for GNNs, reducing annotation costs.
problem Efficiently label nodes on graphs to reduce GNN training costs.
method Formulates as a sequential decision process, trains GNN-based policy network with reinforcement learning.
result Trains a transferable active learning policy that generalizes across different domains.
Proposes an offline active learning method for graphs.
problem Active learning on graphs where labeling is expensive.
method Two-stage biased sampling strategy considering informativeness and representativeness.
result Establishes a theoretical relationship between generalization error and number of nodes selected.
Improves graph-based active learning for non-Gaussian models.
problem Efficiently selecting data points for labeling in graph-based semi-supervised learning.
method Approximates non-Gaussian distributions, introduces rank-one update and model change acquisition function.
result Enhanced active learning for graph-based SSL under non-Gaussian models.
New algorithm improves graph-based active learning by identifying unexplored regions.
problem Improving graph-based active learning by identifying unexplored regions.
method Poisson Reweighted Laplacian Uncertainty Sampling (PWLL) with a diagonal perturbation.
result PWLL effectively identifies unexplored regions in graph-based data.
While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features. Here we propose Graph Spectral Regularization for making hidden layers more interpretable without significantly impacting performance on th…
ActiLabel learns activity patterns across diverse sensor devices.
problem Limited adoption of activity recognition models across different domains due to diverse sensor devices.
method Combination of graph model and optimal tiered mapping for learning activity labels.
result Superior performance compared to state-of-the-art methods on public datasets.
Improved graph-based semi-supervised learning with model change active learning.
problem Identifying which unlabelled data points to label to best improve classifier performance.
method Pairing model change active learning with graph-based semi-supervised learning methods.
result Improved multiclass classification performance over prior methods.
Improves node classification in graphs with active learning.
problem Difficult or expensive labeling in node classification tasks.
method Graph cognizant logistic regression and preemptive query generation.
result Significant improvement over state-of-the-art approaches.
New approach models computer network activity as mixtures of sources.
problem Malicious activity detection in computer networks using standard algorithms is ineffective.
method Source separation approach to model short-term dynamics of computer network activity.
result Qualitative and quantitative experiments validate the approach.
Study extends GNN VC dimension bounds to Pfaffian activation functions.
problem Bounding GNN VC dimension for new activation functions.
method Pfaffian function theory applied to GNNs with sigmoid and hyperbolic tangent activations.
result Bounds on GNN VC dimension for various architectures and graph properties.
Paper proposes a novel graph AL method using contrastive learning.
problem Discovering informative nodes for GNNs with unlabeled data.
method Integrates graph AL with contrastive learning, focusing on homophilous subgraphs.
result Method outperforms state-of-the-arts on five public datasets.
Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learningcs.LG
Enhanced network threat detection using KG, LLM, and imbalanced learning.
problem Challenges in network threat detection due to complex attack patterns and limited historical data.
method Integrated framework combining Knowledge Graph, Imbalanced Learning, and Large Language Model.
result Improved threat capture rate by 3%-4% and increased interpretability of risk predictions.
Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform inference on data that is associated to a graph structure, such as, e.g., citation networks or knowledge graphs. While several variants of GNNs have been proposed, they only consider simple nonlinear activation functions in th…
Develops active intervals for geodesics in Teichmüller space.
problem Understanding geodesics in Teichmüller space with no backtracking.
method Defines active intervals for subsurfaces along geodesics in Thurston metric.
result Active intervals represent reparametrized quasi-geodesics in curve graphs with bounded movement outside.
Proposes active learning for meta-learning in graph node response prediction.
problem Difficulty in improving performance with meta-learning due to unbalanced observations.
method Combines graph convolutional neural networks and reinforcement learning for both prediction and node selection.
result Can predict responses and select nodes even for unseen response variables.
We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements. We propose a hierarchical partitioning of the graph that groups the activated vertices into few partitions, so that a top-down sensing procedure can identify these partitions, and hence the activa…
Dirichlet Active Learningstat.ML
DiAL uses Bayesian Dirichlet random fields for active learning with sparse labels.
problem Active learning with limited labeled data.
method Bayesian Dirichlet random field for feature-conditional class probabilities, calibrating with graph Laplacian.
result Competitive performance in low-label rate graph learning tasks.
Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data. However, a large quantity of labeled graphs is difficult to obtain, which significantly limits the true success of GNNs. Although …
We investigate the problem of active learning on a given tree whose nodes are assigned binary labels in an adversarial way. Inspired by recent results by Guillory and Bilmes, we characterize (up to constant factors) the optimal placement of queries so to minimize the mistakes made on the non-queried nodes. Our query se…
This paper investigates the problem of active learning for binary label prediction on a graph. We introduce a simple and label-efficient algorithm called S2 for this task. At each step, S2 selects the vertex to be labeled based on the structure of the graph and all previously gathered labels. Specifically, S2 queries f…
Improved EXACT strategy reduces GNN memory consumption and runtime.
problem Efficiently training large-scale GNNs with reduced memory usage.
method Block-wise quantization of intermediate activation maps with improved variance minimization.
result Further reduction in memory consumption (>15%) and runtime speedup (5%) with similar performance trade-offs.
This paper studies graphical model selection, i.e., the problem of estimating a graph of statistical relationships among a collection of random variables. Conventional graphical model selection algorithms are passive, i.e., they require all the measurements to have been collected before processing begins. We propose an…
Active learning aims to reduce labeling efforts by selectively asking humans to annotate the most important data points from an unlabeled pool and is an example of human-machine interaction. Though active learning has been extensively researched for classification and ranking problems, it is relatively understudied for…
Active learning (AL) on attributed graphs has received increasing attention with the prevalence of graph-structured data. Although AL has been widely studied for alleviating label sparsity issues with the conventional non-related data, how to make it effective over attributed graphs remains an open research question. E…
Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of the main forum devoted to specific health topics. The changing nature of the users' activities in different subforums can be strong indicators…
MetAL improves graph classification models with fewer labeled data.
problem Efficiently selecting unlabeled graph instances for training.
method Formulates AL as bilevel optimization, uses meta-learning to approximate model performance.
result MetAL outperforms existing AL algorithms on multiple graph datasets.
The detection of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks. Beyond its wide applicability, graph structured anomaly detection serves as a case study in the difficulty of bal…
Machine learning is often used in virtual screening to find compounds that are pharmacologically active on a target protein. The weave module is a type of graph convolutional deep neural network that uses not only features focusing on atoms alone (atom features) but also features focusing on atom pairs (pair features);…
Study compares GNNs and classical molecular featurisations for molecular property and cliff prediction.
problem Comparing GNNs and classical featurisations for molecular property and cliff prediction.
method Systematic exploration and comparison of PDVs, ECFPs, and GNNs; introduction of substructure pooling.
result Sort & Slice outperforms hash-based folding in ECFP vectorization.
We consider the problem of offline, pool-based active semi-supervised learning on graphs. This problem is important when the labeled data is scarce and expensive whereas unlabeled data is easily available. The data points are represented by the vertices of an undirected graph with the similarity between them captured b…
A new criterion for deep active learning selects minimal labeled data points.
problem Efficiently select minimal labeled data points for deep neural networks.
method Diffuses label information over a graph of data representations to switch between exploration and refinement.
result The diffusion-based criterion outperforms existing methods in deep active learning.
Criteria found for graph drawings on surfaces.
problem Graph drawings on surfaces.
method Criteria for integer and modulo 2 embeddability.
result Found criteria for graph drawings on surfaces.
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networkscs.LG
GRM uses graph neural networks to score process activity relevance.
problem Improving business processes with performance measures.
method Graph Relevance Miner (GRM) based on graph neural networks.
result Quantitatively evaluated relevance scores with four datasets.
Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be processed efficiently in terms of both time and space. Current semi-supervised graph…
Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchatcs.LG
While mobile social apps have become increasingly important in people's daily life, we have limited understanding on what motivates users to engage with these apps. In this paper, we answer the question whether users' in-app activity patterns help inform their future app engagement (e.g., active days in a future time w…
Paper introduces a new method to identify brain hubs using both structural and functional connectivity.
problem Hub node identification in brain networks using only functional connectivity.
method Graph signal processing framework that models functional activity as graph signals on structural connectivity.
result The proposed GraFHub framework identifies hub nodes more accurately than conventional methods.
QSAR models struggle to predict activity cliffs, but graph isomorphism features improve AC-sensitivity.
problem QSAR models struggle to predict activity cliffs (ACs).
method Nine distinct QSAR models combining molecular representation methods and regression techniques.
result Graph isomorphism features improve AC-sensitivity.
New algorithm finds corrupted vertices in graphs with few queries.
problem Adversarial tampering of graph edges and vertices.
method Active learning algorithm with polynomial query complexity.
result Efficiently recovers corrupted vertices with small query complexity.
Active graph-based semi-supervised learning (AG-SSL) aims to select a small set of labeled examples and utilize their graph-based relation to other unlabeled examples to aid in machine learning tasks. It is also closely related to the sampling theory in graph signal processing. In this paper, we revisit the original fo…
We consider the problem of estimating the underlying graph associated with a Markov random field, with the added twist that the decoding algorithm can iteratively choose which subsets of nodes to sample based on the previous samples, resulting in an active learning setting. Considering both Ising and Gaussian models, w…
We consider the detection of activations over graphs under Gaussian noise, where signals are piece-wise constant over the graph. Despite the wide applicability of such a detection algorithm, there has been little success in the development of computationally feasible methods with proveable theoretical guarantees for ge…
Agent uses message passing to optimize robot navigation, balancing exploration and exploitation.
problem Optimizing robot navigation in continuous-valued spaces with uncertainty.
method Autoregressive active inference agent using message passing on a factor graph.
result Agent modulates action based on predictive uncertainty, leading to better model of dynamics.
This paper presents an algorithm to construct a weighted adjacency matrix of a plane bipartite graph obtained from a pretzel knot diagram. The determinant of this matrix after evaluation is shown to be the Jones polynomial of the pretzel knot by way of perfect matchings (or dimers) of this graph. The weights are Tutte'…
New method for distributed online learning with communication constraints reduces joint regret.
problem Joint regret minimization in a distributed online learning setting with communication constraints.
method Adaptive graph partitioning and comparator-adaptive online convex optimization with delayed gradient information.
result Optimal graph partition selection for adversarial activations and gradients reduces joint regret.
Machine Learning (ML) has been applied to enable many life-assisting appli-cations, such as abnormality detection and emdergency request for the soli-tary elderly. However, in most cases machine learning algorithms depend on the layout of the target Internet of Things (IoT) sensor network. Hence, to deploy an applicati…
We propose a hypergraph-based active learning scheme which we term , generalizes the previously reported algorithm originally proposed for graph-based active learning with pointwise queries [Dasarathy et al., COLT 2015]. Our method can accommodate hypergraph structures and allows one to ask bo…
Scattering GCN improves graph neural networks by filtering oversmoothing.
problem Oversmoothing in GCNs limits their ability to distinguish graph nodes.
method Augmenting GCNs with geometric scattering transforms and residual convolutions.
result Scattering GCN outperforms GAT in semi-supervised node classification.