Deep learning approaches for similarity computation: A survey
The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …
common but vital task in various domains, such as data mining, machine learning and so on …
Graphsail: Graph structure aware incremental learning for recommender systems
Given the convenience of collecting information through online services, recommender
systems now consume large scale data and play a more important role in improving user …
systems now consume large scale data and play a more important role in improving user …
Trafficstream: A streaming traffic flow forecasting framework based on graph neural networks and continual learning
X Chen, J Wang, K Xie - arXiv preprint arXiv:2106.06273, 2021 - arxiv.org
With the rapid growth of traffic sensors deployed, a massive amount of traffic flow data are
collected, revealing the long-term evolution of traffic flows and the gradual expansion of …
collected, revealing the long-term evolution of traffic flows and the gradual expansion of …
Hierarchical prototype networks for continual graph representation learning
Despite significant advances in graph representation learning, little attention has been paid
to the more practical continual learning scenario in which new categories of nodes (eg, new …
to the more practical continual learning scenario in which new categories of nodes (eg, new …
H2mn: Graph similarity learning with hierarchical hypergraph matching networks
Graph similarity learning, which measures the similarities between a pair of graph-structured
objects, lies at the core of various machine learning tasks such as graph classification …
objects, lies at the core of various machine learning tasks such as graph classification …
Multivariate time-series classification with hierarchical variational graph pooling
In recent years, multivariate time-series classification (MTSC) has attracted considerable
attention owing to the advancement of sensing technology. Existing deep-learning-based …
attention owing to the advancement of sensing technology. Existing deep-learning-based …
Structure aware experience replay for incremental learning in graph-based recommender systems
Large-scale recommender systems are integral parts of many services. With the recent rapid
growth of accessible data, the need for efficient training methods has arisen. Given the high …
growth of accessible data, the need for efficient training methods has arisen. Given the high …
Graph structure aware contrastive knowledge distillation for incremental learning in recommender systems
Personalized recommender systems are playing an increasingly important role for online
services. Graph Neural Network (GNN) based recommender models have demonstrated a …
services. Graph Neural Network (GNN) based recommender models have demonstrated a …
Tie: A framework for embedding-based incremental temporal knowledge graph completion
Reasoning in a temporal knowledge graph (TKG) is a critical task for information retrieval
and semantic search. It is particularly challenging when the TKG is updated frequently. The …
and semantic search. It is particularly challenging when the TKG is updated frequently. The …
scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding
Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables
diverse transcriptional programs to arise from a single genetic blueprint, whereas its …
diverse transcriptional programs to arise from a single genetic blueprint, whereas its …