Papers Learning Network Representations
“Learning Network Representations” 태그가 달린 논문 10편 · 필터 해제
BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky Social
Decentralized social media platforms like Bluesky Social (Bluesky) have made it possible to publicly disclose some user behaviors with millisecond-level precision. Embracing Bluesky's principles of open-source and open-d…
BlockingGraph Neural NetworkLearning Network RepresentationsNetwork Community Partition+2Learning Network Representations with Disentangled Graph Auto-Encoder
The (variational) graph auto-encoder is widely used to learn representations for graph-structured data. However, the formation of real-world graphs is a complicated and heterogeneous process influenced by latent factors.…
DecoderLearning Network RepresentationsPrivacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes
Although recent network representation learning (NRL) works in text-attributed networks demonstrated superior performance for various graph inference tasks, learning network representations could always raise privacy con…
Graph ReconstructionInference AttackLearning Network RepresentationsMembership Inference Attack+3Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data
A key challenge to gaining insight into complex systems is inferring nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting component…
Causal DiscoveryCausal InferenceCommunity DetectionLearning Network Representations+2MFNets: Data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources
We present an approach for constructing a surrogate from ensembles of information sources of varying cost and accuracy. The multifidelity surrogate encodes connections between information sources as a directed acyclic gr…
AllLearning Network RepresentationsGlobal and Local Feature Learning for Ego-Network Analysis
In an ego-network, an individual (ego) organizes its friends (alters) in different groups (social circles). This social network can be efficiently analyzed after learning representations of the ego and its alters in a lo…
Language ModelingLanguage ModellingLearning Network RepresentationsDynamic Joint Variational Graph Autoencoders
Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as dyna…
ClusteringGraph ClusteringGraph EmbeddingLearning Network Representations+2Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysis
The field of Text-to-Speech has experienced huge improvements last years benefiting from deep learning techniques. Producing realistic speech becomes possible now. As a consequence, the research on the control of the exp…
Emotional Speech SynthesisExpressive Speech SynthesisLearning Network RepresentationsSpeech Emotion Recognition+4Learning Features of Network Structures Using Graphlets
Networks are fundamental to the study of complex systems, ranging from social contacts, message transactions, to biological regulations and economical networks. In many realistic applications, these networks may vary ove…
General ClassificationLearning Network RepresentationsNetwork EmbeddingNetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks
Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges …
Anomaly DetectionLearning Network RepresentationsNetwork Embedding