A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This work tackles community detection in networks with node attributes, achieving exact recovery.
New method combines hypergraph structure and node attributes for better community detection.
New method learns fair representations by separating out protected attributes.
User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior anal…
Network embedding is the process of learning low-dimensional representations for nodes in a network, while preserving node features. Existing studies only leverage network structure information and focus on preserving structural features. However, nodes in real-world networks often have a rich set of attributes providi…
Proposes GNAN model for detecting various network structures.
Graph structured data provide two-fold information: graph structures and node attributes. Numerous graph-based algorithms rely on both information to achieve success in supervised tasks, such as node classification and link prediction. However, node attributes could be missing or incomplete, which significantly deterio…
WGNN learns graph representations from incomplete attribute data.
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
DEAL model predicts links for new nodes with only attribute info.
Bayesian approach scores influential training examples for model predictions.
New method uses information bottleneck to quantify variable importance in neural networks.
Crowdsourced data used in machine learning services might carry sensitive information about attributes that users do not want to share. Various methods have been proposed to minimize the potential information leakage of sensitive attributes while maximizing the task accuracy. However, little is known about the theory b…
DVA framework attributes value of predictive models to features, configurations, and interactions.
Graph neural networks (GNNs) have shown great power in learning on attributed graphs. However, it is still a challenge for GNNs to utilize information faraway from the source node. Moreover, general GNNs require graph attributes as input, so they cannot be appled to plain graphs. In the paper, we propose new models nam…
HyperBERT enhances BERT for node classification on text-attributed hypergraphs.
This work addresses fairness in ML models by training and evaluating attribute classifiers under uncertain and incomplete data.
The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circumstances in which nodes have an associated vector of continuous attributes that are also used to learn the node-…
TaCo prevents non-linear classifiers from detecting sensitive attributes.
New framework for fair ranking with noisy protected attributes.
New bounds on machine learning data leakage identified.
Proposes DAPr framework to learn feature importance from prior knowledge.
Proposes a block-based model for attributed network embedding.
In this work, we address the problem of modifying textual attributes of sentences. Given an input sentence and a set of attribute labels, we attempt to generate sentences that are compatible with the conditioning information. To ensure that the model generates content compatible sentences, we introduce a reconstruction…
Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty in fully leveraging attribute information due to practical challenges such as heterogeneity and sparseness. These approaches also fail to c…
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…
Paper defends sensitive attributes in GNNs from inference attacks.
Most approaches aiming to ensure a model's fairness with respect to a protected attribute (such as gender or race) assume to know the true value of the attribute for every data point. In this paper, we ask to what extent fairness interventions can be effective even when only imperfect information about the protected at…
In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new ite…
We propose a neural network architecture for learning vector representations of hotels. Unlike previous works, which typically only use user click information for learning item embeddings, we propose a framework that combines several sources of data, including user clicks, hotel attributes (e.g., property type, star ra…
Develops a method to ensure fairness across multiple sensitive attributes in machine learning.
Proposes methods for local clustering in attributed graphs.
We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-s…
PASS protects private attributes by stochastically substituting data.
Most companies utilize demographic information to develop their strategy in a market. However, such information is not available to most retail companies. Several studies have been conducted to predict the demographic attributes of users from their transaction histories, but they have some limitations. First, they focu…
Enhances community detection in correlated networks with node attributes.
Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes. Low-rank proximities and node features are successfully leveraged in existing GCNs, however, attributes that graph links may carry are commonly ignored, as almost …
DArtNet predicts time series data using graph structure and dynamic attributes.
Method detects anomalies on attributed graphs with few labeled instances.
New framework analyzes pre-stock jump trading behaviors using multivariate time series analysis.
New method separates graph structure from node attributes to recover lost signal.
Proposes a method to predict node attributes using network topology.
Imputation with missing-indicators improves machine learning performance, especially for categorical attributes.
Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applicatio…
We tackle the problem disentangling the latent space of an autoencoder in order to separate labelled attribute information from other characteristic information. This then allows us to change selected attributes while preserving other information. Our method, matrix subspace projection, is much simpler than previous ap…
This paper presents the Variation Network (VarNet), a generative model providing means to manipulate the high-level attributes of a given input. The originality of our approach is that VarNet is not only capable of handling pre-defined attributes but can also learn the relevant attributes of the dataset by itself. Thes…
Wavelet Attribution Method (WAM) improves feature attribution for deep models.
Develops methods to measure and reduce fairness in datasets with limited protected attribute labels.