A survey on graph kernels
Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …
classification tasks on graphs. This survey gives a comprehensive overview of techniques …
Matching node embeddings for graph similarity
G Nikolentzos, P Meladianos… - Proceedings of the AAAI …, 2017 - ojs.aaai.org
Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph
kernels focus on local properties of graphs and ignore global structure. In this paper, we …
kernels focus on local properties of graphs and ignore global structure. In this paper, we …
A comprehensive analysis of privacy-preserving solutions developed for online social networks
Owning to the massive growth in internet connectivity, smartphone technology, and digital
tools, the use of various online social networks (OSNs) has significantly increased. On the …
tools, the use of various online social networks (OSNs) has significantly increased. On the …
Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop.
AMiner 1 is a free online academic search and mining system, having collected more than
130,000,000 researcher profiles and over 200,000,000 papers from multiple publication …
130,000,000 researcher profiles and over 200,000,000 papers from multiple publication …
Graph kernels: A survey
Graph kernels have attracted a lot of attention during the last decade, and have evolved into
a rapidly developing branch of learning on structured data. During the past 20 years, the …
a rapidly developing branch of learning on structured data. During the past 20 years, the …
Name disambiguation in anonymized graphs using network embedding
B Zhang, M Al Hasan - Proceedings of the 2017 ACM on Conference on …, 2017 - dl.acm.org
In real-world, our DNA is unique but many people share names. This phenomenon often
causes erroneous aggregation of documents of multiple persons who are namesake of one …
causes erroneous aggregation of documents of multiple persons who are namesake of one …
Big graphs: challenges and opportunities
W Fan - Proceedings of the VLDB Endowment, 2022 - dl.acm.org
Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it
comes to big graphs, these challenges become even more staggering. Each and every of …
comes to big graphs, these challenges become even more staggering. Each and every of …
Global graph kernels using geometric embeddings
Applications of machine learning methods increasingly deal with graph structured data
through kernels. Most existing graph kernels compare graphs in terms of features defined on …
through kernels. Most existing graph kernels compare graphs in terms of features defined on …
Author name disambiguation on heterogeneous information network with adversarial representation learning
Author name ambiguity causes inadequacy and inconvenience in academic information
retrieval, which raises the necessity of author name disambiguation (AND). Existing AND …
retrieval, which raises the necessity of author name disambiguation (AND). Existing AND …
Entity reconciliation in big data sources: A systematic mapping study
The entity reconciliation (ER) problem aroused much interest as a research topic in today's
Big Data era, full of big and open heterogeneous data sources. This problem poses when …
Big Data era, full of big and open heterogeneous data sources. This problem poses when …