Graph Embeddings for One-pass Processing of Heterogeneous Queries

Duong, Chi Thang; Yin, Hongzhi; Hoang, Dung; Nguyen, Minn Hung; Weidlich, Matthias; Nguyen, Quoc Viet Hung; Aberer, Karl

doi:10.1109/icde48307.2020.00222

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…The performance of exop GCN is compared with other 13 GNN methods (The hyper-parameters setting for exop GCN and other GNNs is shown in S1 File ). These 13 methods are GCN [ 6 ], FastGCN [ 11 ], GAT [ 30 ], SGC [ 31 ], ClusterGCN [ 12 ], DAGNN [ 32 ], APPNP [ 33 ], SSGC [ 34 ], GraphMLP [ 35 ], RobustGCN [ 36 ], LATGCN [ 37 ], MedianGCN [ 38 ] and ONF (ONFdw and ONFde) [ 18 ]. Some previous methods that deal with attribute-incomplete graphs have been proposed.…”

Section: Resultsmentioning

confidence: 99%

“…SimP-GCN can balance the information from graph structure and node features and achieve better performance on both assortative and disassortative graphs. Duong et al find that a strong correlation between node features and node labels may lead to better performance of GNN, and they propose new feature initialization methods to deal with non-attributed graphs [ 18 ]. Chen et al design a distribution matching named structure-attribute transformer (SAT) to deal with attribute-incomplete graphs.…”

Section: Introductionmentioning

confidence: 99%

“…Many graphs in the real world do not contain any feature information due to privacy concerns or difficulty in collecting node features [ 18 ]. An example is the social networks including REDDIT [ 22 , 23 ] and Karate [ 24 ] data.…”

Section: Introductionmentioning

confidence: 99%

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A simple and effective convolutional operator for node classification without features by graph convolutional networks

Jiao,

Zhang,

et al. 2024

PLoS ONE

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Graph neural networks (GNNs), with their ability to incorporate node features into graph learning, have achieved impressive performance in many graph analysis tasks. However, current GNNs including the popular graph convolutional network (GCN) cannot obtain competitive results on the graphs without node features. In this work, we first introduce path-driven neighborhoods, and then define an extensional adjacency matrix as a convolutional operator. Second, we propose an approach named exopGCN which integrates the simple and effective convolutional operator into GCN to classify the nodes in the graphs without features. Experiments on six real-world graphs without node features indicate that exopGCN achieves better performance than other GNNs on node classification. Furthermore, by adding the simple convolutional operator into 13 GNNs, the accuracy of these methods are improved remarkably, which means that our research can offer a general skill to improve accuracy of GNNs. More importantly, we study the relationship between node classification by GCN without node features and community detection. Extensive experiments including six real-world graphs and nine synthetic graphs demonstrate that the positive relationship between them can provide a new direction on exploring the theories of GCNs.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A simple and effective convolutional operator for node classification without features by graph convolutional networks

Jiao,

Zhang,

et al. 2024

PLoS ONE

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“…While our network embedding can be seen as a deep encoder, it differs from traditional approaches as we also capture the heterogeneity of the nodes in the embeddings. Closest to our work is the HIN embedding proposed in [50]. However, this approach is limited in various aspects: It does not support multiple queries, neglects the transformation from multi-modal data to a HIN, and does not scale to large networks.…”

Section: Network Embeddingmentioning

confidence: 99%

Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

Duong

Nguyen

Yin

et al. 2022

IEEE Trans. Knowl. Data Eng.

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The heterogeneity of today's Web sources requires information retrieval (IR) systems to handle multi-modal queries. Such queries define a user's information needs by different data modalities, such as keywords, hashtags, user profiles, and other media.Recent IR systems answer such a multi-modal query by considering it as a set of separate uni-modal queries. However, depending on the chosen operationalisation, such an approach is inefficient or ineffective. It either requires multiple passes over the data or leads to inaccuracies since the relations between data modalities are neglected in the relevance assessment. To mitigate these challenges, we present an IR system that has been designed to answer genuine multi-modal queries. It relies on a heterogeneous network embedding, so that features from diverse modalities can be incorporated when representing both, a query and the data over which it shall be evaluated. By embedding a query and the data in the same vector space, the relations across modalities are made explicit and exploited for more accurate query evaluation. At the same time, multi-modal queries are answered with a single pass over the data. An experimental evaluation using diverse real-world and synthetic datasets illustrates that our approach returns twice the amount of relevant information compared to baseline techniques, while scaling to large multi-modal databases.Index Terms-query embedding, graph embedding, heterogeneous information network ✦ • A model for heterogeneous data: We introduce a representation of heterogeneous data based on HINs to capture the semantic relations between data of different modalities of the same entity [13], [14]. This includes a

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“…Our GCN-based model consists of k layers, and each hidden feature layer simultaneously encodes the topological and attributional information using a message passing scheme [7]. Most of GCN models use the embeddings in the final layer as the node representation [8], since the deepest layer aggregates the information from all previous layers. However, while the deeper layers contain richer topological information, they are also prone to noise from inconsistent nodes in previous layers, which is fairly common in real-world KG datasets.…”

Section: Introductionmentioning

confidence: 99%

Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks (Extended Abstract)

Tâm

Huynh

Yin

et al. 2021

2021 IEEE 37th International Conference on Data Engineering (ICDE)

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Knowledge graph (KG) entity alignment is the task of identifying corresponding entities across different KGs. Existing alignment techniques often require large amounts of labelled data, are unable to encode multi-modal data simultaneously, and enforce only a few consistency constraints. In this paper, we propose an end-to-end, unsupervised entity alignment framework for cross-lingual KGs using multi-order graph convolutional networks. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of accuracy, efficiency, and label saving.

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Graph Embeddings for One-pass Processing of Heterogeneous Queries

Cited by 5 publications

References 12 publications

A simple and effective convolutional operator for node classification without features by graph convolutional networks

A simple and effective convolutional operator for node classification without features by graph convolutional networks

Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks (Extended Abstract)

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