Graph2Video: Leveraging Video Models to Model Dynamic Graph Evolution

Hua Liu; Yanbin Wei; Fei Xing; Tyler Derr; Haoyu Han; Yu Zhang

cs.CVMar 9, 2026

Graph2Video: Leveraging Video Models to Model Dynamic Graph Evolution

Authors: Hua Liu, Yanbin Wei, Fei Xing, Tyler Derr, Haoyu Han, Yu Zhang

Organizations: Southern University of Science and Technology · The Hong Kong University of Science and Technology · City University of Hong Kong · Vanderbilt University ·

Abstract

Dynamic graphs are common in real-world systems such as social media, recommender systems, and traffic networks. Existing dynamic graph models for link prediction often fall short in capturing the complexity of temporal evolution. They tend to overlook fine-grained variations in temporal interaction order, struggle with dependencies that span long time horizons, and offer limited capability to model pair-specific relational dynamics. To address these challenges, we propose Graph2Video, a video-inspired framework that views the temporal neighborhood of a target link as a sequence of "graph frames". By stacking temporally ordered subgraph frames into a "graph video", Graph2Video leverages the inductive biases of video foundation models to capture both fine-grained local variations and long-range temporal dynamics. It generates a link-level embedding that serves as a lightweight and plug-and-play link-centric memory unit. This embedding integrates seamlessly into existing dynamic graph encoders, effectively addressing the limitations of prior approaches. Extensive experiments on benchmark datasets show that Graph2Video outperforms state-of-the-art baselines on the link prediction task in most cases. The results highlight the potential of borrowing spatio-temporal modeling techniques from computer vision as a promising and effective approach for advancing dynamic graph learning.

Figures & tables

NSS	Datasets	TGN	CAWN	TCL	GraphMixer	DyGFormer	DyGFormer+	TPNet	TPNet+
	Reddit	$98.63_{\pm 0.06}$	$99.11_{\pm 0.01}$	$97.53_{\pm 0.02}$	$97.31_{\pm 0.01}$	$99.22_{\pm 0.01}$	$99.23_{\pm 0.01}$	$\underline{99.27_{\pm 0.00}}$	99.32 ±0.01
	MOOC	$89.15_{\pm 1.60}$	$80.15_{\pm 0.25}$	$82.38_{\pm 0.24}$	$82.78_{\pm 0.15}$	$87.52_{\pm 0.49}$	$87.33_{\pm 0.43}$	96.39 ±0.09	$96.38_{\pm 0.03}$
rnd	Enron	$86.53_{\pm 1.11}$	$89.56_{\pm 0.09}$	$79.70_{\pm 0.71}$	$82.25_{\pm 1.02}$	$92.47_{\pm 0.12}$	$92.32_{\pm 0.18}$	$92.90_{\pm 0.17}$	93.01 ±0.10
	UCI	$92.34_{\pm 1.04}$	$95.18_{\pm 0.06}$	$89.57_{\pm 1.63}$	$93.25_{\pm 0.57}$	$95.79_{\pm 0.17}$	$96.24_{\pm 0.04}$	$97.35_{\pm 0.04}$	97.37 ±0.10
	Can. Parl.	$70.28_{\pm 2.34}$	$69.82_{\pm 2.34}$	$68.67_{\pm 2.67}$	$77.04_{\pm 0.96}$	$97.36_{\pm 0.45}$	$97.60_{\pm 0.40}$	$90.28_{\pm 0.37}$	98.70 ±0.13
	Avg. rank	5.60	6.00	7.60	6.40	3.60	3.40	2.20	1.20

Table 1: AP (%) for transductive dynamic link prediction on real-world datasets with three sampling strategies (NSS).

NSS	Datasets	TGN	CAWN	TCL	GraphMixer	DyGFormer	DyGFormer+	TPNet	TPNet+
	Reddit	$97.50_{\pm 0.07}$	$98.62_{\pm 0.01}$	$94.09_{\pm 0.07}$	$95.26_{\pm 0.02}$	$98.84_{\pm 0.02}$	$98.86_{\pm 0.02}$	$98.86_{\pm 0.01}$	98.95 ±0.03
	MOOC	$89.04_{\pm 1.17}$	$81.42_{\pm 0.24}$	$82.38_{\pm 0.24}$	$81.41_{\pm 0.21}$	$86.96_{\pm 0.43}$	$86.92_{\pm 0.28}$	$95.07_{\pm 0.26}$	95.10 ±0.10
rnd	Enron	$77.94_{\pm 1.02}$	$86.35_{\pm 0.51}$	$79.70_{\pm 0.71}$	$75.88_{\pm 0.48}$	$89.76_{\pm 0.34}$	$89.88_{\pm 0.12}$	90.34 ±0.28	$90.18_{\pm 0.27}$
	UCI	$88.12_{\pm 2.05}$	$92.73_{\pm 0.06}$	$89.57_{\pm 1.63}$	$91.19_{\pm 0.42}$	$94.54_{\pm 0.12}$	$94.89_{\pm 0.04}$	$95.74_{\pm 0.05}$	95.83 ±0.16
	Can. Parl.	$70.88_{\pm 2.34}$	$69.82_{\pm 2.34}$	$68.67_{\pm 2.67}$	$77.04_{\pm 0.46}$	$87.74_{\pm 0.71}$	$87.89_{\pm 1.01}$	$68.09_{\pm 1.55}$	95.50 ±0.49
	Avg. rank	5.80	5.60	6.80	6.60	3.80	3.00	3.00	1.20

Table 2: AP (%) for inductive dynamic link prediction on real-world datasets with three sampling strategies (NSS).

Figure 2: Graph Visualizer and Video Encoder Comparison.

Model	s	Enron	UCI
DyGFormer	8	92.35 ±0.20	96.19 ±0.14
	16	92.32 ±0.18	96.24 ±0.04
	32	92.31 ±0.16	96.10 ±0.38
TPNet	8	93.05 ±0.18	97.23 ±0.21
	16	93.01 ±0.10	97.37 ±0.10
	32	93.02 ±0.19	97.30 ±0.10

Table 3: Performance comparison when varying the number of most recent neighbors

s

in the video frame.

Model	Fusion Strategy	Enron	UCI
DyGFormer	MLP	92.40 ±0.12	96.23 ±0.06
	Bilinear	91.60 ±0.29	96.15 ±0.10
	Attention	92.32 ±0.18	96.24 ±0.04
TPNet	MLP	92.98 ±0.08	97.14 ±0.24
	Bilinear	93.18 ±0.12	97.04 ±0.21
	Attention	93.01 ±0.10	97.37 ±0.10

Table 4: Fusion strategies performance comparison.

Model	Sam2-F	Enron	UCI
DyGFormer	8	92.22 ±0.13	95.93 ±0.06
	16	92.25 ±0.13	95.98 ±0.08
	32	92.31 ±0.14	96.01 ±0.09
TPNet	8	92.82 ±0.09	97.16 ±0.17
	16	92.85 ±0.23	97.19 ±0.29
	32	93.01 ±0.11	97.29 ±0.12

Table 5: Performance comparison when varying the number of Frames

F

in the Sam2 video model.

Datasets	Domains	#Nodes	#Links	#N&L Feat	Bipartite	Duration	Unique Steps	Time Granularity
Reddit	Social	10,984	672,447	– & 172	True	1 month	669,065	Unix timestamps
MOOC	Interaction	7,144	411,749	– & 4	True	17 month	345,600	Unix timestamps
Enron	Social	184	125,235	– & –	False	3 years	22,632	Unix timestamps
UCI	Social	1,899	59,835	– & –	False	196 days	58,911	Unix timestamps
Can. Parl.	Politics	734	74,478	– & 1	False	14 years	14	years

Table 6: Statistics of the five real-world datasets.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

NSS	Datasets	TGN	CAWN	TCL	GraphMixer	DyGFormer	DyGFormer+	TPNet	TPNet+
	Reddit	$98.60_{\pm 0.06}$	$99.01_{\pm 0.01}$	$97.42_{\pm 0.02}$	$97.17_{\pm 0.02}$	$99.15_{\pm 0.01}$	$99.16_{\pm 0.01}$	$99.22_{\pm 0.00}$	$\textbf{99.27}_{\pm 0.01}$
	MOOC	$91.21_{\pm 1.15}$	$80.38_{\pm 0.26}$	$83.12_{\pm 0.18}$	$84.01_{\pm 0.17}$	$87.91_{\pm 0.58}$	$86.93_{\pm 0.38}$	$\textbf{97.17}_{\pm 0.08}$	$97.13_{\pm 0.02}$
rnd	Enron	$88.32_{\pm 0.99}$	$90.45_{\pm 0.14}$	$75.74_{\pm 0.72}$	$84.38_{\pm 0.21}$	$93.33_{\pm 0.13}$	$93.45_{\pm 0.13}$	$93.98_{\pm 0.26}$	$\textbf{94.16}_{\pm 0.10}$
	UCI	$92.03_{\pm 1.13}$	$93.87_{\pm 0.08}$	$87.82_{\pm 1.36}$	$91.81_{\pm 0.67}$	$94.49_{\pm 0.26}$	$95.22_{\pm 0.07}$	$96.79_{\pm 0.05}$	$\textbf{96.86}_{\pm 0.14}$
	Can. Parl.	$76.99_{\pm 1.80}$	$75.70_{\pm 3.27}$	$72.46_{\pm 3.23}$	$83.17_{\pm 0.53}$	$97.76_{\pm 0.41}$	$97.99_{\pm 0.41}$	$92.05_{\pm 0.34}$	$\textbf{98.83}_{\pm 0.12}$
	Avg. rank	5.40	6.00	7.60	6.60	3.80	3.20	2.20	1.20

Appendix

Table 7: AUC (%) for transductive dynamic link prediction on real-world datasets with three sampling strategies (NSS).

NSS	Datasets	TGN	CAWN	TCL	GraphMixer	DyGFormer	DyGFormer+	TPNet	TPNet+
	Reddit	$97.39_{\pm 0.07}$	$98.42_{\pm 0.02}$	$93.80_{\pm 0.07}$	$94.97_{\pm 0.05}$	$98.71_{\pm 0.01}$	$98.71_{\pm 0.01}$	$\underline{98.73_{\pm 0.02}}$	$\textbf{98.83}_{\pm 0.03}$
	MOOC	$91.24_{\pm 0.99}$	$81.86_{\pm 0.25}$	$81.43_{\pm 0.19}$	$82.77_{\pm 0.24}$	$87.62_{\pm 0.51}$	$87.91_{\pm 0.24}$	$\textbf{95.55}_{\pm 0.25}$	$\textbf{95.55}_{\pm 0.15}$
rnd	Enron	$78.83_{\pm 1.11}$	$87.02_{\pm 0.50}$	$72.33_{\pm 0.99}$	$76.51_{\pm 0.71}$	$\underline{90.69_{\pm 0.26}}$	$\textbf{90.78}_{\pm 0.17}$	$90.21_{\pm 0.49}$	$90.18_{\pm 0.48}$
	UCI	$86.68_{\pm 2.29}$	$90.40_{\pm 0.11}$	$84.49_{\pm 1.82}$	$89.30_{\pm 0.57}$	$92.63_{\pm 0.13}$	$93.27_{\pm 0.03}$	$\underline{94.40_{\pm 0.03}}$	$\textbf{94.52}_{\pm 0.26}$
	Can. Parl.	$55.86_{\pm 0.75}$	$58.83_{\pm 1.13}$	$55.83_{\pm 1.07}$	$58.32_{\pm 1.08}$	$89.33_{\pm 0.48}$	$\underline{89.51_{\pm 0.82}}$	$69.21_{\pm 1.31}$	$\textbf{95.69}_{\pm 0.50}$
	Avg. rank	5.80	5.40	8.00	6.40	3.40	2.80	2.60	1.60

Appendix

Table 8: AUC (%) for inductive dynamic link prediction on real-world datasets with three sampling strategies (NSS).

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A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction

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Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. · Tehran Institute for Advanced Studies, Khatam University, Tehran, Iran.

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Predicting links in sparse, continuously evolving networks is a central challenge in network science. Conventional heuristic methods and deep learning models, including Graph Neural Networks (GNNs), are typically designed for static graphs and thus struggle to capture temporal dependencies. Snapshot-based techniques partially address this issue but often encounter data sparsity and class imbalance, particularly in networks with transient interactions such as telecommunication call detail records (CDRs). Temporal Graph Networks (TGNs) model dynamic graphs by updating node embeddings over time; however, their predictive accuracy under sparse conditions remains limited. In this study, we improve the TGN framework by extracting enclosing subgraphs around candidate links, enabling the model to jointly learn structural and temporal information. Experiments on a sparse CDR, email, message dataset show that our approach increases average precision by at least 2% over standard TGNs, demonstrating the advantages of integrating local topology for robust link prediction in dynamic networks.

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