DisenKGAT

Wu, Junkang; Shi, Wentao; Cao, Xuezhi; Chen, Jiawei; Lei, Wenqiang; Zhang, Fuzheng; Wu, Wei; He, Xiangnan

doi:10.1145/3459637.3482424

Cited by 49 publications

(13 citation statements)

References 28 publications

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“…For example, gene embeddings obtained through the KG graph is coupled and this has an impact on both interpretability and performance. To further improve this drawback in future work, we will attempt to use the decoupling representation learning, similar to that described in DisenKGAT ( Wu et al , 2021 ), to achieve higher quality gene embeddings. In addition, due to the sparsity of the SL graph, negative samples were created using theoretical considerations and may contain potential positive samples.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network

et al. 2023

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Motivation Synthetic Lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL interactions from laboratory experiments, an increasing number of research groups are devising computational prediction methods to guide the discovery of potential SL pairs. Although existing methods have attempted to capture the underlying mechanisms of SL interactions, methods that have a deeper understanding of and attempt to explain SL mechanisms still need to be developed. Results In this work, we propose a novel SL prediction method, SLGNN. This method is based on the following assumption: SL interactions are caused by different molecular events or biological processes, which we define as SL-related factors that lead to SL interactions. SLGNN, apart from identifying SL interaction pairs, also models the preferences of genes for different SL-related factors, making the results more interpretable for biologists and clinicians. SLGNN consists of three steps: first, we model the combinations of relationships in the gene-related knowledge graph as the SL-related factors. Next, we derive initial embeddings of genes through an explicit message aggregation process of the knowledge graph. Finally, we derive the final gene embeddings through an SL graph, constructed using known SL gene pairs, utilizing factor-based message aggregation. At this stage, a supervised end-to-end training model is used for SL interaction prediction. Based on experimental results, the proposed SLGNN model outperforms all current state-of-the-art SL prediction methods and provides better interpretability. Availability SLGNN is freely available at https://github.com/zy972014452/SLGNN. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Conclusion and Discussionmentioning

confidence: 99%

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network

et al. 2023

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“…Inspired by recent progress in disentangled representation learning [32,46,48], we seek graph disentanglement for context-aware event forecasting. Most existing works solely rely on the inherent structural information for graph disentanglement.…”

Section: Context-aware Graphmentioning

confidence: 99%

“…Most existing works solely rely on the inherent structural information for graph disentanglement. For example, MaridVAE [33] and DGCF [45] utilize the user-item interactions to learn disentangled representations for different intents; DisenKGAT [48] tackles the heterogeneous knowledge graph and disentangles the entity embedding with respect to different topics and clusters. Nonetheless, these methods are incapable of disentangling event graphs since the events are too coarse-grained, and pure structural information is unable to well disentangle the graph.…”

Section: Context-aware Graphmentioning

confidence: 99%

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Context-aware Event Forecasting via Graph Disentanglement

Ma,

Ye,

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Event forecasting has been a demanding and challenging task throughout the entire human history. It plays a pivotal role in crisis alarming and disaster prevention in various aspects of the whole society. The task of event forecasting aims to model the relational and temporal patterns based on historical events and makes forecasting to what will happen in the future. Most existing studies on event forecasting formulate it as a problem of link prediction on temporal event graphs. However, such pure structured formulation suffers from two main limitations: 1) most events fall into general and high-level types in the event ontology, and therefore they tend to be coarse-grained and offers little utility which inevitably harms the forecasting accuracy; and 2) the events defined by a fixed ontology are unable to retain the out-of-ontology contextual information.To address these limitations, we propose a novel task of contextaware event forecasting which incorporates auxiliary contextual information. First, the categorical context provides supplementary fine-grained information to the coarse-grained events. Second and more importantly, the context provides additional information towards specific situation and condition, which is crucial or even determinant to what will happen next. However, it is challenging to properly integrate context into the event forecasting framework, considering the complex patterns in the multi-context scenario. Towards this end, we design a novel framework named Separation and Collaboration Graph Disentanglement (short as SeCoGD) for context-aware event forecasting. In the separation stage, we leverage the context as a prior guidance to disentangle the event graph into multiple sub-graphs, followed by a context-specific module to model the relational-temporal patterns within each context. In the collaboration stage, we design a cross-context module to retain the collaborative associations among multiple contexts. Since there is no available dataset for this novel task, we construct three largescale datasets based on GDELT. Experimental results demonstrate that our model outperforms a list of SOTA methods. The dataset and code are released via https://github.com/yecchen/SeCoGD.

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“…Graph Neural Network (GNN) is an emerging deep learning method for graph-structured data presentation [11]. Recently, it has been successfully applied in different fields of graphstructured data, like recommendation system [12], social network [13], knowledge map [14], etc. However, little attention has been paid to introducing GNN into IP geolocation.…”

Section: Measurement-based Ip Geolocation Can Geolocate a Targetmentioning

confidence: 99%

GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

Ding¹,

Luo²,

Wang

et al. 2023

IEEE Trans. Netw. Sci. Eng.

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Rule-based fine-grained IP geolocation methods are hard to generalize in computer networks which do not follow hypothetical rules. Recently, deep learning methods, like multi-layer perceptron (MLP), are tried to increase generalization capabilities. However, MLP is not so suitable for graph-structured data like networks. MLP treats IP addresses as isolated instances and ignores the connection information, which limits geolocation accuracy. In this work, we research how to increase the generalization capability with an emerging graph deep learning method -Graph Neural Network (GNN). First, IP geolocation is re-formulated as an attributed graph node regression problem. Then, we propose a GNN-based IP geolocation framework named GNN-Geo. GNN-Geo consists of a preprocessor, an encoder, messaging passing (MP) layers and a decoder. The preprocessor and encoder transform measurement data into the initial node embeddings. MP layers refine the initial node embeddings by modeling the connection information. The decoder maps the refined embeddings to nodes' locations and relieves the convergence problem by considering prior knowledge. The experiments in 8 real-world IPv4/IPv6 networks in North America, Europe and Asia show the proposed GNN-Geo clearly outperforms the state-of-art rule-based and learning-based baselines. This work verifies the great potential of GNN for fine-grained IP geolocation.

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DisenKGAT

Cited by 49 publications

References 28 publications

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network

Context-aware Event Forecasting via Graph Disentanglement

GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

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