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Wikipedia Mathematics

Improving Prediction of Daily Visits of Wikipedia Mathematics Topics using Graph Neural Networks

Number of Daily Visits of Wikipedia Mathematics Topics using Graph-based Convolutional Neural Network as a Robust Machine Learning Model

Abstract

Spatiotemporal signal processing is one of the complex and hot topics especially in web mining like web traffics analysis. The web pages and their links are a graph and their visits or content can be a signal. The PyTorch Geometric Temporal is introduced for spatiotemporal signal mining. This study analyze Wikipedia mathematics pages using PyTorch Geometric Temporal library to improve their visits prediction during the time using grid search for parameter adjustment. The results show 1.15% relative improvement for GConvGRU algorithm versus basic related work which published the library.

Keywords— Web Mining; Traffic Prediction; Graph Convolutional Network (GCN).

Benchmark

# lags train ratio k linear digit node features filters lr epoch time error
1 14 50% 2 1 14 32 0.01 50 911s 0.8143236637115479
2 14 50% 3 1 14 32 0.01 50 1444s 0.8163800835609436
3 14 50% 4 1 14 32 0.01 50 1947s 0.7932114601135254
4 28 50% 1 1 28 32 0.01 50 441s 0.8761430382728577
5 42 50% 1 1 42 32 0.01 50 443s 0.8508368134498596
6 56 50% 1 1 56 32 0.01 50 461s 0.856105387210846
7 70 50% 1 1 70 32 0.01 50 505s 0.8762531280517578
8 84 50% 1 1 84 32 0.01 50 529s 0.9409999847412109
9 98 50% 1 1 98 32 0.01 50 547s 0.9203919768333435
10 14 50% 2 1 14 32 0.02 50 936s 0.8355252742767334
11 14 50% 3 1 14 32 0.02 50 1839s 0.8604558110237122
12 14 50% 4 1 14 32 0.02 50 2346s 0.8616055846214294
13 14 50% 5 1 14 32 0.02 50 2559s 0.8867608308792114
14 14 50% 10 1 14 32 0.02 50 5376s 0.8464503288269043
15 56 50% 2 1 56 32 0.01 50 1296s 0.8364545106887817
16 70 50% 2 1 70 32 0.01 50 1358s 0.8788001537322998
17 84 50% 2 1 84 32 0.01 50 1185s 0.9005643129348755
18 98 50% 2 1 98 32 0.01 50 1216s 0.8543722629547119
19 42 50% 2 1 42 32 0.01 50 1114s 0.8399303555488586
20 28 50% 2 1 28 32 0.01 50 1050s 0.8465337753295898
21 14 50% 1 1 14 50 0.02 50 464s 0.8963724374771118
22 16 70% 1 1 16 16 0.01 50 608s 1.401132583618164
23 32 70% 1 1 32 16 0.01 50 607s 1.634675145149231
24 16 70% 1 1 16 16 0.01 50 591s 1.3993479013442993
25 64 70% 1 1 64 16 0.01 50 629s 1.669908046722412
26 128 70% 1 1 128 16 0.01 50 659s 1.0828124284744263
27 256 70% 1 1 256 16 0.01 50 668s 0.8271479606628418
28 32 70% 1 1 32 16 0.01 50 606s 1.685264229774475
39 32 70% 2 1 32 16 0.01 50 1326s 1.3383041620254517
30 32 70% 3 1 32 16 0.01 50 2049s 1.3266639709472656
31 2 70% 1 1 2 16 0.01 50 612s 1.2748934030532837
32 4 70% 1 1 4 16 0.01 50 623s 1.3384982347488403
33 8 70% 1 1 8 16 0.01 50 580s 1.364047884941101
34 16 70% 1 1 16 16 0.01 50 582s 1.3909107446670532
35 16 70% 1 1 16 2 0.01 50 565s 1.2858407497406006
36 16 70% 1 1 16 4 0.01 50 601s 1.3470855951309204
37 16 70% 1 1 16 8 0.01 50 608s 1.3956334590911865
38 16 70% 1 1 16 16 0.01 50 624s 1.3498746156692505
39 16 70% 1 1 16 32 0.01 50 639s 1.3010109663009644
40 16 70% 1 1 16 32 0.02 50 629s 1.7191174030303955
41 16 70% 1 1 16 32 0.03 50 648s 1.809025764465332
42 16 70% 1 1 16 16 0.01 50 623s 1.4078537225723267
43 14 30% 1 1 14 32 0.01 50 268s 1.0906275510787964
44 14 40% 1 1 14 32 0.01 50 362s 0.8774722814559937
45 14 60% 1 1 14 32 0.01 50 532s 0.8744056224822998
46 14 70% 1 1 14 32 0.01 50 632s 1.314452052116394
47 14 90% 1 1 14 32 0.01 50 783s 0.66766756772995
48 16 30% 1 1 16 16 0.01 50 271s 1.089638352394104
49 16 40% 1 1 16 16 0.01 50 345s 0.8601189255714417
50 16 50% 1 1 16 16 0.01 50 419s 0.8372963070869446
51 16 60% 1 1 16 16 0.01 50 517s 0.8800567984580994
52 16 70% 1 1 16 16 0.01 50 600s 1.3647654056549072
53 16 80% 1 1 16 16 0.01 50 677s 0.8518020510673523
54 16 90% 1 1 16 16 0.01 50 781s 0.6800107955932617
55 16 70% 1 1 16 64 0.01 50 671s 1.4077645540237427
56 64 70% 1 1 64 2 0.01 50 643s 1.4670355319976807
57 64 70% 1 1 64 4 0.01 50 620s 1.1776090860366821
58 64 70% 1 1 64 8 0.01 50 601s 1.4935400485992432
59 64 70% 1 1 64 16 0.01 50 597s 1.6202009916305542
60 64 70% 1 1 64 32 0.01 50 619s 1.52037513256073
61 64 70% 1 1 64 64 0.01 50 653s 1.5570083856582642
62 64 70% 1 1 64 128 0.01 50 697s 1.526889443397522
63 256 90% 3 1 256 2 0.01 50 4868s 0.6845269799232483
64 256 90% 3 1 256 2 0.005 50 4505s 0.6575148701667786
65 256 90% 3 1 256 2 0.0025 50 4059s 0.6614260077476501
66 256 90% 3 1 256 4 0.01 50 4500s 0.7011445164680481
67 256 90% 2 1 256 2 0.005 50 2333s 0.6535002589225769
68 256 90% 2 1 256 4 0.01 50 2359s 0.6499081254005432
69 256 90% 2 1 256 4 0.0025 50 2519s 0.7072554230690002
70 256 90% 2 1 256 4 0.005 50 2576s 0.6859683990478516
71 16 70% 1 1 16 16 0.01 50 285s 1.3943161964416504
72 16 70% 1 1 16 16 0.01 50 582s 1.3705955743789673
73 16 70% 1 1 16 16 0.01 100 1160s 1.3797444105148315
74 16 70% 1 1 16 16 0.01 150 1741s 1.4409031867980957
75 16 70% 1 1 16 16 0.01 200 2284s 1.4262229204177856

Authors

  • Seyyed Ali Mohammadiyeh

    • Department of Pure Mathematics, Faculty of Mathematical Sciences

    • University of Kashan, Kashan, Iran

    • alim [at] kashanu.ac.ir

  • Dr. Behzad Soleimani Neysiani

    • Technical Soldier, Department of Research and Development

    • Ava Aria Information Company, Demis Holding, Isfahan, Iran,

    • b.soleimani [at] demisco.com