W-MetaPath2Vec: The topic-driven meta-path-based model for large-scaled content-based heterogeneous information network representation learning
Recently, heterogeneous network representation learning has attracted a lot of attentions
due to its potential applications. Our works in this paper are concentrated on how to …
due to its potential applications. Our works in this paper are concentrated on how to …
Discovering topics in Twitter about the COVID-19 outbreak in Spain
MM Agüero-Torales, D Vilares… - … del lenguaje natural, 2021 - journal.sepln.org
In this work, we apply topic modeling to study what users have been discussing in Twitter
during the beginning of the COVID-19 pandemic. More particularly, we explore the period of …
during the beginning of the COVID-19 pandemic. More particularly, we explore the period of …
W-KG2Vec: a weighted text-enhanced meta-path-based knowledge graph embedding for similarity search
Recently, similar entity searching over knowledge graph (KG) has gained much attentions
by researchers. However, in rich-semantic KGs with multi-typed entities and relations, also …
by researchers. However, in rich-semantic KGs with multi-typed entities and relations, also …
[HTML][HTML] Hydrology research articles are becoming more topically diverse
Abstract We used Natural Language Processing (NLP) to assess topic diversity in all
research articles (∼ 75,000) from eighteen water science and hydrology journals published …
research articles (∼ 75,000) from eighteen water science and hydrology journals published …
W-MMP2Vec: topic-driven network embedding model for link prediction in content-based heterogeneous information network
Link prediction on heterogeneous information network (HIN) is considered as a challenge
problem due to the complexity and diversity in types of nodes and links. Currently, there are …
problem due to the complexity and diversity in types of nodes and links. Currently, there are …
W-Metagraph2Vec: a novel approval of enriched schematic topic-driven heterogeneous information network embedding
Recently, heterogeneous information network (HIN) embedding is wide studied due to its
various applications. In general, network embedding is a way of representation network's …
various applications. In general, network embedding is a way of representation network's …
DW-PathSim: a distributed computing model for topic-driven weighted meta-path-based similarity measure in a large-scale content-based heterogeneous information …
From the past, several studies in the information network mining have been mainly designed
for single-typed objects and links, called the homogeneous information network (HoIN) …
for single-typed objects and links, called the homogeneous information network (HoIN) …
Semantic enhanced top-k similarity search on heterogeneous information networks
Similarity search on heterogeneous information networks has attracted widely attention from
both industrial and academic areas in recent years, for example, used as friend detection in …
both industrial and academic areas in recent years, for example, used as friend detection in …
[HTML][HTML] Semantic enhanced Top-k similarity search on weighted HIN
Y Zhang, M Yu, T Zhang, G Yu - Neural Computing and Applications, 2022 - Springer
Similarity searches on heterogeneous information networks (HINs) have attracted wide
attention from both industrial and academic areas in recent years; for example, they have …
attention from both industrial and academic areas in recent years; for example, they have …
W-PathSim++: the novel approach of topic-driven similarity search in large-scaled heterogeneous network with the support of Spark-based DataLog
Similarity measurement on objects in a HIN is considered as a challenging problem of
networked data mining. There are several proposed models that are used to compute the …
networked data mining. There are several proposed models that are used to compute the …