CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?

Jie Wang; Zheng Yan; Jiahe Lan; Xuyan Li; Elisa Bertino

cs.LGDec 12, 2025

CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?

Authors: Jie Wang, Zheng Yan, Jiahe Lan, Xuyan Li, Elisa Bertino

Organizations: State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University · Hangzhou Institute of Technology, Xidian University · Department of Computer Science, Purdue University

Abstract

Trust prediction provides valuable support for decision-making, risk mitigation, and system security enhancement. Recently, Graph Neural Networks (GNNs) have emerged as a promising approach for trust prediction, owing to their ability to learn expressive node representations that capture intricate trust relationships within a network. However, current GNN-based trust prediction models face several limitations: (i) Most of them fail to capture trust dynamicity, leading to questionable inferences. (ii) They rarely consider the heterogeneous nature of real-world networks, resulting in a loss of rich semantics. (iii) None of them support context-awareness, a basic property of trust, making prediction results coarse-grained. To this end, we propose CAT, the first Context-Aware GNN-based Trust prediction model that supports trust dynamicity and accurately represents real-world heterogeneity. CAT consists of a graph construction layer, an embedding layer, a heterogeneous attention layer, and a prediction layer. It handles dynamic graphs using continuous-time representations and captures temporal information through a time encoding function. To model graph heterogeneity and leverage semantic information, CAT employs a dual attention mechanism that identifies the importance of different node types and nodes within each type. For context-awareness, we introduce a new notion of meta-paths to extract contextual features. By constructing context embeddings and integrating a context-aware aggregator, CAT can predict both context-aware trust and overall trust. Extensive experiments on three real-world datasets demonstrate that CAT outperforms five groups of baselines in trust prediction, while exhibiting strong scalability to large-scale graphs and robustness against both trust-oriented and GNN-oriented attacks.

Figures & tables

Models	Guardian [ 6 ]	Medley [ 7 ]	GATrust [ 8 ]	TrustGNN [ 9 ]	KGTrust [ 10 ]	DTrust [ 11 ]	TrustGuard [ 2 ]	CAT
Dynamicity	○	●	○	○	○	●	●	●
Heterogeneity	○	○	○	○	●	○	○	●
Context-Awareness	○	○	○	○	○	○	○	●
Robustness	○	○	○	○	○	○	◐	●

TABLE I: Comparison between CAT and existing GNN-based trust prediction models.

Fig. 1: A motivating example of dynamic heterogeneous networks represented by a real-world social network with interactions involving users $u_{1}\sim u_{4}$ and items across various contexts $c_{1}\sim c_{3}$ , including sports, electronics, and clothing. Dashed and solid arrows indicate interactions occurring at different time points.

Datasets	# Users	# Items	# Ratings	# Trust relationships	# Contexts	Timestamps of ratings	Timestamps of trust relationships
Epinions	9163	12573	265189	311158	25	✓	✓
Ciao	2378	16861	36065	57544	6	✓	✗
CiaoDVD	19533	16121	72665	40133	17	✓	✗

TABLE II: Statistics of Epinions, Ciao, and CiaoDVD datasets.

Fig. 3: Results of context-aware trust prediction on the Ciao dataset. Case 1 illustrates a user pair $\langle v_{i},v_{j}\rangle$ with an overall distrusted relationship, whereas case 2 depicts a user pair with an overall trusted relationship.

CAT and variants	Epinions: Observed users			Epinions: Unobserved users			Ciao
CAT and variants	MRR	AP	AUC	MRR	AP	AUC	MRR	AP	AUC
CAT	0.6025	0.9383	0.9677	0.4082	0.9527	0.8933	0.4150	0.9234	0.9327
w/o Time Embedding	0.5575	0.9240	0.9597	0.2441	0.9040	0.8045	0.3702	0.9050	0.9142
w/o Type Attention	0.5941	0.9368	0.9677	0.3033	0.9393	0.8776	0.4035	0.9216	0.9322
w/o Node Attention	0.5781	0.9328	0.9653	0.3975	0.9501	0.8851	0.4042	0.9194	0.9293
w/o Ca Meta-path	0.5763	0.9274	0.9600	0.2924	0.9328	0.8592	0.3959	0.9157	0.9256

TABLE V: Ablation studies on CAT.

CAT and variants	Epinions: Observed users			Epinions: Unobserved users			Ciao
CAT and variants	MRR	AP	AUC	MRR	AP	AUC	MRR	AP	AUC
CAT	0.6025	0.9383	0.9677	0.4082	0.9527	0.8933	0.4150	0.9234	0.9327
w/o Time Embedding	0.5575	0.9240	0.9597	0.2441	0.9040	0.8045	0.3702	0.9050	0.9142
w/o Type Attention	0.5941	0.9368	0.9677	0.3033	0.9393	0.8776	0.4035	0.9216	0.9322
w/o Node Attention	0.5781	0.9328	0.9653	0.3975	0.9501	0.8851	0.4042	0.9194	0.9293
w/o Ca Meta-path	0.5763	0.9274	0.9600	0.2924	0.9328	0.8592	0.3959	0.9157	0.9256

TABLE V: Ablation studies on CAT.

Tasks	Models	Clean	Trust-oriented Attacks					GNN-oriented Attacks
Tasks	Models	Clean	$p$ =5%	$p$ =10%	$p$ =15%	$p$ =20%	MDR $\downarrow$	$p$ =5%	$p$ =10%	$p$ =15%	$p$ =20%	MDR $\downarrow$
①	Medley	0.4762	0.4552	0.4367	0.4155	0.4079	14.34%	0.4650	0.4576	0.4477	0.4393	7.75%
	TrustGuard	0.4955	0.4831	0.4820	0.4565	0.4528	8.62%	0.4813	0.4711	0.4664	0.4721	5.87%
	CAT	0.6025	0.5968	0.6046	0.6144	0.6070	0.95%	0.5999	0.5869	0.5842	0.5821	3.39%
②	Medley	0.1979	0.1894	0.1791	0.1756	0.1715	13.34%	0.1909	0.1890	0.1846	0.1737	12.23%
	TrustGuard	0.2571	0.2465	0.2431	0.2253	0.2250	12.49%	0.2477	0.2296	0.2348	0.2289	10.97%

TABLE VI: Robustness comparison under trust-oriented and GNN-oriented attacks (MRR results).

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Metrics	Tasks	Models	Clean	Trust-oriented Attacks					GNN-oriented Attacks
Metrics	Tasks	Models	Clean	$p$ =5%	$p$ =10%	$p$ =15%	$p$ =20%	MDR $\downarrow$	$p$ =5%	$p$ =10%	$p$ =15%	$p$ =20%	MDR $\downarrow$
AP	①	Medley	0.8944	0.8862	0.8855	0.8728	0.8613	3.70%	0.8923	0.8838	0.8827	0.8810	1.50%
		TrustGuard	0.8919	0.8860	0.8855	0.8724	0.8685	2.62%	0.8853	0.8816	0.8826	0.8814	1.18%
		CAT	0.9383	0.9394	0.9391	0.9415	0.9401	-0.09%	0.9399	0.9350	0.9326	0.9335	0.61%
	②	Medley	0.8884	0.8714	0.8473	0.8430	0.8328	6.26%	0.8799	0.8758	0.8780	0.8652	2.61%
		TrustGuard	0.8950	0.8918	0.8918	0.8824	0.8802	1.65%	0.8901	0.8881	0.8898	0.8881	0.77%

Appendix

TABLE VII: Robustness comparison under trust-oriented and GNN-oriented attacks (AP and AUC results).

Fig. 9: Visualization of the dual attention scores, including type attention scores (shown in blue) and node attention scores (shown in red). $v_{0}\sim v_{3}$ denote users and $v_{4}\sim v_{6}$ denote items, with $v_{0}$ being the target node.

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