MDNE embeds network structures and attributes for better analysis.
problem Preserving both structural and attribute features in network embedding.
method Multimodal Deep Network Embedding (MDNE) using deep model with multiple layers of non-linear functions.
result MDNE outperforms baselines on various tasks with real-world datasets.
AnomalyDAE detects anomalies in networks by learning cross-modality interactions.
problem Detecting anomalies in attributed networks where structure and attributes interact.
method Dual autoencoder framework with attention mechanism for joint learning of structure and attribute embeddings.
result AnomalyDAE effectively detects anomalies by reconstructing node attributes and structures.
New method separates graph structure from node attributes to recover lost signal.
problem Standard representation learning on attributed graphs merges incompatible metric spaces, leading to geometrically flawed alignment.
method Custom variational autoencoder that separates manifold learning from structural alignment.
result Transforms geometric conflict into interpretable structural descriptor, uncovering connectivity patterns and anomalies.
Paper tackles attribute pattern learning in high-dimensional SLAMs.
problem Learning significant attribute patterns from high-dimensional SLAMs.
method Proposes a penalized likelihood method for selecting attribute patterns.
result Establishes selection consistency in overfitted SLAMs.
GUIDE detects anomalies in attributed networks by reconstructing node attributes and higher-order structures.
problem Lack of effective mechanisms for detecting anomalies in complex network interactions.
method GUIDE uses attribute and structure autoencoders, graph attention, and reconstruction errors to identify anomalies.
result GUIDE significantly outperforms state-of-the-art methods on multiple real-world datasets.
Generates missing node attributes for better graph-based tasks.
problem Missing or incomplete node attributes degrade graph-based algorithms' performance.
method Deep adversarial learning-based method (NANG) to generate node attributes.
result Generated node attributes improve node classification and link prediction.
Proposes GNAN model for detecting various network structures.
problem Detecting traditional communities in networks.
method GNAN model combining topology and node-attribute information.
result GNAN detects a broader range of network structures.
Paper learns latent and hierarchical structures in CDMs from data.
problem Jointly learning latent and hierarchical structures in CDMs from observed data.
method Penalized likelihood approach for selecting attributes and estimating structures; EM and latent structure recovery algorithms.
result Good performance demonstrated by simulation and real data applications.
New method combines hypergraph structure and node attributes for better community detection.
problem Improving community detection in hypergraphs with node attributes.
method Developed a principled model that learns from data to combine higher-order interactions and node attributes.
result Strong performance in hyperedge prediction and community detection, especially when attributes are informative.
Paper tackles product categorization with structured and unstructured attributes for large-scale eCommerce.
problem Challenges in categorizing products with thousands of classes and millions of products.
method Compares hierarchical and flat models, uses Deep Learning for feature extraction, combines structured and unstructured attributes.
result Flat models perform better in specific cases, and the proposed approach handles faulty attribute names and values.
GraphVRNN generates graphs with latent variables and node attributes.
problem Generating diverse and complex graph structures.
method Probabilistic autoregressive model for graph generation.
result GraphVRNN can model complicated distributions and generate plausible structures and node attributes.
Adaptive graph convolution improves attributed graph clustering performance.
problem Joint modeling of graph structures and node attributes is challenging.
method Adaptive graph convolution that captures global cluster structure and selects appropriate order for different graphs.
result Empirical results show our method compares favorably with state-of-the-art methods.
A new model detects complex network communities using node attributes.
problem Lack of methods integrating node attributes for community detection in attributed networks.
method BCSBM model that integrates betweenness centrality and clustering coefficient of nodes.
result BCSBM model outperforms other methods in detecting various network structures.
Graph attention auto-encoder reconstructs graph structure and attributes.
problem Lack of methods to reconstruct graph structure and node attributes in graph auto-encoders.
method Stacked encoder/decoder layers with self-attention mechanisms, regularized node representations to reconstruct graph structure.
result Competitive performance on node classification benchmarks, including inductive learning.
HyperBERT enhances BERT for node classification on text-attributed hypergraphs.
problem Challenges in capturing hypergraph structure and text attributes in node classification.
method Mixing hypergraph-aware layers with BERT for improved node classification.
result HyperBERT achieves state-of-the-art results on text-attributed hypergraph benchmarks.
This paper addresses identifiability issues in HLAMs with hierarchical constraints.
problem Identifiability of HLAMs with hierarchical constraints.
method Developed sufficient and necessary identifiability conditions.
result Characterizes impacts of different attribute types in the graph on identifiability.
Concept modulation models unify identifiability and extrapolation in conditional latent variable models.
problem Reliable generalization in conditional latent variable models
method Concept modulation models (CMMs) with structure AoΛoCoX result Lifts identifiability to conditional settings and controls extrapolation through attribute potentials.
New framework for attributing online marketing touchpoints.
problem Fine-grained attribution of individual touchpoints' effects.
method Graphical point process framework for studying conversion effects.
result Proposed methods allocate proper credit to touchpoints for each customer's path.
DEAL model predicts links for new nodes with only attribute info.
problem Predicting links for new nodes with only attribute info.
method DEAL model with two encoders and alignment mechanism.
result DEAL significantly outperforms existing methods on inductive link prediction.
Framework learns dynamic graph attributes and links co-evolution.
problem Forecasting change of node attributes and link formation in dynamic graphs.
method CoEvoGNN framework with temporal self-attention and joint optimization.
result Framework outperforms baselines on predicting unseen graph snapshots.
Proposes methods for local clustering in attributed graphs.
problem Finding a single cluster concentrated on a specific region in a graph.
method Introduces Graph Unimodality (GU) and Attribute Unimodality (AU) measures, and LOCLU algorithm to optimize Compactness score.
result Local cluster detected by LOCLU concentrates on the region of interest and exhibits unimodal data distribution.
This paper introduces a new method to cluster qualitative attribute data using tree structures.
problem Clustering qualitative attribute data, especially when values are not in Euclidean space.
method Developed a joint learning mechanism to iteratively learn trees representing qualitative values' order relationships.
result The joint learning mechanism successfully clusters qualitative attribute data, yielding accurate results.
GLACE embeds large-scale attributed graphs effectively, preserving structure and attributes.
problem Uncertainty and complexity in large-scale attributed graphs.
method Gaussian embeddings for scalable and efficient graph embedding.
result GLACE outperforms state-of-the-art methods on multiple graph analysis tasks.
A new method for disentangled latent spaces in VAEs that can manipulate attributes.
problem Disentangled representation of attributes in latent spaces of VAEs.
method Attribute-based regularization loss to enforce monotonic relationships between attributes and latent codes.
result Manipulation of attributes in latent spaces post-training.
New attribution method for neural networks using causal principles.
problem Understanding the impact of features on neural network outputs.
method Viewing neural networks as Structural Causal Models, computing causal effects efficiently.
result Efficient computation of feature impacts on neural network outputs.
Proposes a method to predict node attributes using network topology.
problem Predicting node attributes in graphs for various applications.
method Creates a feature map using all attributes of neighbors to predict attributes values for a node.
result Significantly improves prediction accuracy compared to baseline approaches.
Proposes a block-based model for attributed network embedding.
problem Handles both assortative and disassortative networks.
method Assigns nodes to blocks based on similar linkage patterns, using neural networks to preserve attribute information.
result Consistently outperforms state-of-the-art methods on disassortative networks.
Deep neural network algorithms are difficult to analyze because they lack structure allowing to understand the properties of underlying transforms and invariants. Multiscale hierarchical convolutional networks are structured deep convolutional networks where layers are indexed by progressively higher dimensional attrib…
Wavelet Attribution Method (WAM) improves feature attribution for deep models.
problem Inability of pixel-based heatmaps to capture data structure and variability in feature attribution.
method Wavelet domain for feature attribution, leveraging spatial and scale-localized properties of wavelet coefficients.
result WAM provides quantitatively superior explanations across audio, image, and volume modalities.
Method detects anomalies on attributed graphs with few labeled instances.
problem Detecting anomalies on connected instances (attributed graphs) with limited labeled data.
method Embed nodes in latent space using GCNs, training to distinguish normal and anomalous nodes.
result Method outperforms existing methods on real-world attributed graph datasets.
Improved scalability and interpretability in training data attribution.
problem Identifying which training data drives specific behaviors, especially unintended ones.
method Leveraging interpretable structures within the model to attribute model behavior to semantic directions, not individual test examples.
result Simple probe-based attribution methods are first-order approximations of Concept Influence that achieve comparable performance while being over an order-of-magnitude faster.
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
problem Lack of mutual affinity exploitation between nodes and attributes in graph convolution.
method Dual variational auto-encoders for node and attribute embeddings, Gaussian mixture model priors, mutual distance and clustering assignment hardening losses.
result Demonstrates effectiveness on real-world attributed network datasets.
Community detection algorithms are fundamental tools to understand organizational principles in social networks. With the increasing power of social media platforms, when detecting communities there are two possi- ble sources of information one can use: the structure of social network and node attributes. However struc…
System helps scientists visualize deep learning model of x-ray images.
problem Understanding complex x-ray scattering images with multiple attributes.
method Interactive visualization system in feature space and classification output.
result Users can explore and compare images and attributes flexibly.
Paper analyzes multi-attribute data to estimate differences in Gaussian graphical models.
problem Estimating differences in two Gaussian graphical models with similar structure.
method Group lasso penalized D-trace loss function and ADMM algorithm for optimization.
result Consistency in support recovery and estimation in high-dimensional settings established.
CDA framework infers channel influence from aggregated data without user identifiers.
problem Lack of user-level path data due to privacy regulations and platform restrictions.
method CDA integrates PCMCI for causal discovery and Structural Causal Model for effect estimation.
result CDA achieves strong accuracy in estimating channel influence, even under structural uncertainty.
DGBO optimizes attributed graphs efficiently.
problem Optimizing graphs with rich contextual features.
method Deep graph neural network surrogate for scalable Bayesian optimization.
result DGBO scales linearly with observations and outperforms state-of-the-art methods.
Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated…
SLOGAN improves GANs' conditional generation by balancing latent attribute distributions.
problem Imbalanced latent attribute distributions in GANs.
method Stein latent optimization with a Gaussian mixture prior and contrastive loss.
result SLOGAN achieves state-of-the-art unsupervised conditional generation performance.
The paper tackles multi-level fairness in algorithmic systems, addressing bias at both individual and structural levels.
problem Algorithmic systems can unfairly impact marginalized groups, especially when considering only individual-level bias.
method Formalizes multi-level fairness using causal inference tools, addressing effects of sensitive attributes at multiple levels.
result Illustrates the importance of accounting for macro-level sensitive attributes in fairness assessments.
Proposes a method for explaining black-box models with nested feature attributions.
problem Making black-box models transparent and trustworthy.
method Model-agnostic local explanation method exploiting nested feature structure and consistency property.
result Accurate and consistent HiFAs and LoFAs estimated using fewer model queries.
Paper defends sensitive attributes in GNNs from inference attacks.
problem Protecting sensitive attributes in GNNs from inference attacks.
method Proposes adversarial training with TV and Wasserstein distance to locally filter sensitive attributes.
result Framework creates strong defense against inference attacks with minimal performance loss.
Enhances community detection in correlated networks with node attributes.
problem Community detection in multiple networks with correlated node attributes and edges.
method Introduced the correlated Contextual Stochastic Block Model (CSBM), developed a two-step matching procedure.
result Algorithm recovers exact node correspondence, enabling enhanced community detection.
This work tackles community detection in networks with node attributes, achieving exact recovery.
problem Community detection in networks with correlated node attributes.
method Information-theoretic criterion and iterative clustering algorithm maximizing joint likelihood.
result Exact recovery of community labels under a general model for network and node attributes.
New method for interpreting financial model risks.
problem Fairly allocating risk in financial models.
method Extending Shapley value framework for axiomatic risk attribution.
result Risk can be well allocated in financial models.
Paper estimates differences in multi-attribute Gaussian graphical models using non-convex penalties.
problem Estimating differences in multi-attribute Gaussian graphical models with similar structure.
method Penalized D-trace loss function with non-convex (log-sum and SCAD) penalties, proximal gradient descent methods.
result Theoretical analysis and numerical examples support consistency in support recovery and estimation.
A-DOGE embeds attributed graphs efficiently using density of states.
problem Efficiently represent node-attributed graphs with few numerical features.
method A-DOGE uses density of states to blend topology and attributes, leveraging efficient approximation algorithms.
result A-DOGE achieves competitive performance with modern supervised GNNs while being significantly faster.
Study uncovers bias in image classification models using attribution maps.
problem Data bias in image classification models.
method Created an artificial dataset with known bias, trained CNNs, and used attribution maps to inspect decisions.
result Different attribution map techniques highlight bias better than others, and metrics support bias identification.