Hierarchical graph embedding in vector space by graph pyramid

Mousavi, Seyedeh Fatemeh; Safayani, Mehran; Mirzaei, Abdolreza; Bahonar, Hoda

doi:10.1016/j.patcog.2016.07.043

Cited by 34 publications

(20 citation statements)

References 28 publications

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“…Whole-graph embedding benefits the graph classification task by providing a straightforward and efficient solution for calculating graph similarities [49], [55], [95]. To establish a compromise between the embedding time (efficiency) and the ability to preserve information (expressiveness), [95] designs a hierarchical graph embedding framework. It thinks that accurate understanding of the global graph information requires the processing of substructures in different scales.…”

Section: Whole-graph Embeddingmentioning

confidence: 99%

“…For example, [4] learns edge-based embedding via minimizing the margin-based ranking loss (Sec Apart from the introduced five categories of techniques, there exist other approaches. [95] presents embedding of a graph by its distances to prototype graphs. [16] first embeds a few landmark nodes using their pairwise shortest path distances.…”

Section: Hybrid Techniques and Othersmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai

Zheng

Chang

2018

IEEE Trans. Knowl. Data Eng.

1,716

885

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Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.

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Section: Whole-graph Embeddingmentioning

confidence: 99%

Section: Hybrid Techniques and Othersmentioning

confidence: 99%

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai

Zheng

Chang

2018

IEEE Trans. Knowl. Data Eng.

1,716

885

View full text Add to dashboard Cite

show abstract

“…Motivated by them, Broelemann et al [50,56] proposed two closely related approaches based on hierarchical graph for error tolerant matching of graphical symbols. Recently, Mousavi et al [14] proposed a graph embedding strategy based on hierarchical graph representation. They claimed that the proposed framework is generic enough to incorporate any kind of graph embedding technique.…”

Section: Hierarchical Graph Representationmentioning

confidence: 99%

“…No abstract information is taken into consideration, hence, Φ(H G ) = ϕ(G). Pyramidal embedding: This embedding has been previously proposed in the literature [14,58]. It combines information of the abstract levels of the graph i.e.…”

Section: Baseline Embeddingmentioning

confidence: 99%

Hierarchical stochastic graphlet embedding for graph-based pattern recognition

Dutta

Riba

Lladós

et al. 2019

Neural Comput & Applic

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Despite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable with many machine learning tools. This is because of the incompatibility of most of the mathematical operations in graph domain. Graph embedding has been proposed as a way to tackle these difficulties, which maps graphs to a vector space and makes the standard machine learning techniques applicable for them. However, it is well known that graph embedding techniques usually suffer from the loss of structural information. In this paper, given a graph, we consider its hierarchical structure for mapping it into a vector space. The hierarchical structure is constructed by topologically clustering the graph nodes, and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure of graph is constructed, we consider its various configurations of its parts, and use stochastic graphlet embedding (SGE) for mapping them into vector space. Broadly speaking, SGE produces a distribution of uniformly sampled low to high order graphlets as a way to embed graphs into the vector space.In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched through the distribution of low to high order stochastic graphlets complements each other and include important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, and it is not a surprise that we obtain more robust vector space embedding of graphs. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods.

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“…The results of community detection considering content resource do not reflect detailed interest opinions. Users in the same community are not emotionally congruent [12]. Therefore, identifying similar emotional interest users based on a community for socialized recommendations is a problem.…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommendations Based on Sentimental Interest Community Detection

Zheng

Wang

2018

Scientific Programming

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Communities have become a popular platform of mining interests for recommender systems. The semantics of topics reflect users' implicit interests. Sentiments on topics imply users' sentimental tendency. People with common sentiments can form resonant communities of interest. In this paper, a resonant sentimental interest community-based recommendation model is proposed to improve the accuracy performance of recommender systems. First, we learn the weighted semantics vector and sentiment vector to model semantic and sentimental user profiles. Then, by combining semantic and sentimental factors, resonance relationship is computed to evaluate the resonance relationship of users. Finally, based on resonance relationships, resonant community is detected to discover a resonance group to make personalized recommendations. Experimental results show that the proposed model is more effective in finding semantics-related sentimental interests than traditional methods.

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Hierarchical graph embedding in vector space by graph pyramid

Cited by 34 publications

References 28 publications

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Hierarchical stochastic graphlet embedding for graph-based pattern recognition

Personalized Recommendations Based on Sentimental Interest Community Detection

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