Titlebook: Network Embedding; Theories, Methods, a Cheng Yang,Chuan Shi,Maosong Sun Book 2021 Springer Nature Switzerland AG 2021

Rejuvenate

Network Embedding for Large-Scale Graphsf multi-label classification and link prediction, where baselines and our model have the same memory usage. Compared with baseline methods, COSINE has up to 23% increase on classification and up to 25% increase on link prediction. Moreover, time of all representation learning methods using COSINE de

柔聲地說

Network Embedding for Heterogeneous Graphsdistinctive characteristics of relations, we propose different models specifically tailored to handle ARs and IRs in RHINE, which can better capture the structures and semantics of the networks. Finally, we combine and optimize these models in a unified and elegant manner. Extensive experiments on t

睨視

Network Embedding for Recommendation Systems on LBSNsopt a network embedding method for the construction of social networks. Second, we consider four factors that influence the generation process of mobile trajectories, namely user visit preference, influence of friends, short-term sequential contexts, and long-term sequential contexts. Finally, the t

助記

Abduct

ordain

畫布

tackle

Network Embedding for Graphs with Node Attributesll applied with typical representation learning methods. Taking text feature as an example, we will introduce text-associated DeepWalk (TADW) model for learning NEs with node attributes in this chapter. Inspired by the proof that DeepWalk, a state-of-the-art network representation method, is actuall

MORPH

并置

Network Embedding for Graphs with Node Contentsork and citation network, nodes have rich text content which can be used to analyze their semantic aspects. In this chapter, we assume that a node usually shows different aspects when interacting with different neighbors (context), and thus should be assigned different embeddings. However, most exis