SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 8: Additional generalization experiments: Test results on different network topologies after one training on a real network, where the original performance represents the test performance of DDMSL on real networks.
Training data	Cora MI	Power Grid	PGP	Twitter	Douban
Network	PR	RE	F1	AUC	PR	RE	F1	AUC	PR	RE	F1	AUC	PR	RE	F1	AUC	PR	RE	F1	AUC
Original performance	DDMSL	0.790	0.908	0.845	0.941	0.763	0.966	0.852	0.966	0.754	0.887	0.815	0.928	0.445	0.286	0.313	0.625	0.484	0.324	0.381	0.622
SLVAE	0.721	0.765	0.852	0.908	0.908	0.719	0.803	0.856	0.817	0.721	0.766	0.851	0.310	0.317	0.253	0.578	0.412	0.140	0.209	0.547
Small World	DDMSL	0.732	0.826	0.776	0.896	0.812	0.499	0.62	0.744	0.987	0.684	0.808	0.841	0.375	0.299	0.290	0.618	0.409	0.295	0.301	0.624
SLVAE	0.824	0.576	0.678	0.781	0.626	0.335	0.436	0.656	0.982	0.539	0.696	0.769	0.308	0.241	0.227	0.572	0.375	0.124	0.186	0.544
ER	DDMSL	0.722	0.584	0.645	0.779	0.349	0.687	0.463	0.773	0.997	0.614	0.632	0.731	0.439	0.289	0.309	0.624	0.320	0.367	0.307	0.614
SLVAE	0.894	0.586	0.708	0.789	0.747	0.409	0.528	0.697	0.956	0.555	0.703	0.776	0.310	0.311	0.285	0.569	0.368	0.118	0.179	0.537
BA Tree	DDMSL	0.482	0.832	0.609	0.866	0.872	0.774	0.82	0.881	0.947	0.672	0.786	0.834	0.327	0.377	0.323	0.612	0.451	0.289	0.314	0.623
SLVAE	0.961	0.577	0.721	0.787	0.939	0.399	0.560	0.698	0.993	0.548	0.708	0.774	0.252	0.285	0.193	0.517	0.312	0.126	0.180	0.525
BA Dense	DDMSL	0.749	0.683	0.715	0.829	0.654	0.354	0.459	0.667	0.972	0.598	0.741	0.798	0.369	0.305	0.288	0.622	0.423	0.289	0.304	0.623
SLVAE	0.662	0.611	0.635	0.788	0.731	0.441	0.550	0.712	0.997	0.446	0.617	0.723	0.286	0.405	0.315	0.567	0.374	0.132	0.195	0.553
Time Complexity. Lastly, we conducted a comparative evaluation of the time complexity between DDMSL and baseline algorithms across diverse datasets, revealing the outcomes illustrated in Table 9. Owing to the gradual inference of diffusion state for each time step, the time complexity of DDMSL tends to be substantial. However, optimization through parallel computing can effectively mitigate this disparity.
Table 9: Additional Time complexity experiment.
Test time	Cora-MI	Power-Grid	PGP
DDMSL	15.84s	22.19s	22.72s
SLVAE	4.77s	7.28s	11.29s
DDMIX	9.34s	15.7s	23.26s
GCNSI	1.4s	8.14s	15.41s
LPSI	2m14s	1m51s	21m37s
OJC	6m11s	50m17s	2h41m52s
NetSleuth	2m40s	4m39s	21m24s
Training Time	Cora-MI	Power-Grid	PGP
DDMSL	10m36s	16m57s	32m03s
SLVAE	18s	39s	1m10s
DDMIX	16m5s	24m17s	37m53s
GCNSI	3m53	20m6s	34m26s
H Visualization
H.1 Visualization of reconstructing diffusion paths
To conserve space, we displayed the actual node states and corresponding prediction results every  $20\%$  of the time. Figures 5 to 9 showcase the results, where the blue nodes denote susceptible ones, the red nodes denote infected nodes, and the green nodes denote recovered nodes. The findings indicate that DDMSL accurately restored the node states at different times. In contrast, DDMIX could only restore infected nodes, revealing that DDMSL far surpasses DDMIX in its expression capability.
Figure 5: DDMSL reconstructs SIR diffusion on Karate.
H.2 Visual comparison of source localization
Due to spatial limitations, we only presented the source localization results of DDMSL and benchmark algorithms on the Karate and Jazz datasets. The results are depicted in Figures 10 and 11. The baseline algorithm's performance is unsatisfactory, as evidenced by significant positioning errors in the source nodes. On the contrary, DDMSL outperforms other benchmark algorithms in accurately identifying source nodes. Moreover, even when misidentifying source nodes, DDMSL locates them near the actual nodes.
Figure 6: DDMSL reconstructs SIR diffusion on Jazz.
Figure 7: DDMSL reconstructs SIR diffusion on Coral ml.
Figure 8: DDMSL reconstructs SIR diffusion on Power grid.
Figure 9: DDMSL reconstructs SIR diffusion on PGP.
(a) DDMSL
(b) SLVAE
(c) DDmix
(d) LPSI
(e) GCNSI
(f) OJC
(g) Ground Truth
Figure 10: Visualization comparisons of source localization on Karate.
(a) DDMSL
(e) GCNSI
Figure 11: Visualization comparisons of source localization on Jazz.