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The potential impact of a paper is often quantified by how many citations it will receive.
Predictive aspects of a stochastic model for citation processes
Glänzel, W., Schubert, A., 1995 · 1995
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Citations and the zipf–mandelbrot law
Silagadze, Z., 1997 · 1997
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Supervised neural networks for the classification of structures
Sperduti, A., Starita, A., 1997 · 1997
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Pmc open access subset
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Sleeping beauties in science
Van Raan, A.F., 2004 · 2004
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A new model for learning in graph domains, in: Proceedings. 2005 IEEE international joint conference on neural networks, pp. 729–734
Gori, M., Monfardini, G., Scarselli, F., 2005 · 2005
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G., 2008 · 2008
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Arnetminer: extraction and mining of academic social networks, in: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 990–998
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z., 2008 · 2008
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Open access and global participation in science
Evans, J.A., Reimer, J., 2009 · 2009
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Citation count prediction: learning to estimate future citations for literature, in: Proceedings of the 20th ACM international conference on Information and knowledge management, pp. 1247–1252
Yan, R., Tang, J., Liu, X., Shan, D., Li, X., 2011 · 2011
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Information diffusion in online social networks: A survey
Guille, A., Hacid, H., Favre, C., Zighed, D.A., 2013 · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., 2013 · 2013
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Quantifying long-term scientific impact
Wang, D., Song, C., Barabási, A.L., 2013 · 2013
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
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Deepwalk: Online learning of social representations, in: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 701–710
Perozzi, B., Al-Rfou, R., Skiena, S., 2014 · 2014
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A survey on predicting the popularity of web content
Tatar, A., De Amorim, M.D., Fdida, S., Antoniadis, P., 2014 · 2014
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Citation impact prediction for scientific papers using stepwise regression analysis
Yu, T., Yu, G., Li, P.Y., Wang, L., 2014 · 2014
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Seismic: A self-exciting point process model for predicting tweet popularity, in: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1513–1522
Zhao, Q., Erdogdu, M.A., He, H.Y., Rajaraman, A., Leskovec, J., 2015 · 2015
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Quantifying the evolution of individual scientific impact
Sinatra, R., Wang, D., Deville, P., Song, C., Barabási, A.L., 2016 · 2016
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Deephawkes: Bridging the gap between prediction and understanding of information cascades, in: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1149–1158
Cao, Q., Shen, H., Cen, K., Ouyang, W., Cheng, X., 2017 · 2017
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A dynamic network measure of technological change
Funk, R.J., Owen-Smith, J., 2017 · 2017
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Deepcas: An end-to-end predictor of information cascades, in: Proceedings of the 26th international conference on World Wide Web, pp. 577–586
Predicting the citation counts of individual papers via a bp neural network
Ruan, X., Zhu, Y., Li, J., Cheng, Y., 2020 · 2020
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Dysat: Deep neural representation learning on dynamic graphs via self-attention networks, in: Proceedings of the 13th international conference on web search and data mining, pp. 519–527
Sankar, A., Wu, Y., Gou, L., Zhang, W., Yang, H., 2020 · 2020
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y., 2020 · 2020
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How attentive are graph attention networks?, in: International Conference on Learning Representations
Brody, S., Alon, U., Yahav, E., 2021 · 2021
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Slowed canonical progress in large fields of science
Chu, J.S., Evans, J.A., 2021 · 2021
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Li, C., Ma, J., Guo, X., Mei, Q., 2017 · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I., 2017 · 2017
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Modeling relational data with graph convolutional networks, in: European semantic web conference, Springer. pp. 593–607
Schlichtkrull, M., Kipf, T.N., Bloem, P., Van Den Berg, R., Titov, I., Welling, M., 2018 · 2018
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How powerful are graph neural networks?, in: International Conference on Learning Representations
Xu, K., Hu, W., Leskovec, J., Jegelka, S., 2018 · 2018
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Predicting citation counts based on deep neural network learning techniques
Abrishami, A., Aliakbary, S., 2019 · 2019
Cited alongside, same era.
Scibert: A pretrained language model for scientific text, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3615–3620
Beltagy, I., Lo, K., Cohan, A., 2019 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186
Devlin, J., Chang, M.W., Lee, K., Toutanova, K., 2019 · 2019
Cited alongside, same era.
Schubert: Scholarly document chunks with bert-encoding boost citation count prediction, in: First Workshop on Scholarly Document Processing, Association for Computational Linguistics (ACL). pp. 148–157
van Dongen, T., de Buy Wenniger, G.M., Schomaker, L., 2020 · 2020
Cited alongside, same era.
Hints: citation time series prediction for new publications via dynamic heterogeneous information network embedding, in: Proceedings of the Web Conference 2021, pp. 3158–3167
Jiang, S., Koch, B., Sun, Y., 2021 · 2021
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A deep-learning based citation count prediction model with paper metadata semantic features
Ma, A., Liu, Y., Xu, X., Dong, T., 2021 · 2021
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A survey of information cascade analysis: Models, predictions, and recent advances
Zhou, F., Xu, X., Trajcevski, G., Zhang, K., 2021 · 2021
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Multi-scale graph capsule with influence attention for information cascades prediction
Chen, X., Zhang, F., Zhou, F., Bonsangue, M., 2022 · 2022
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Fine-grained citation count prediction via a transformer-based model with among-attention mechanism
Huang, S., Huang, Y., Bu, Y., Lu, W., Qian, J., Wang, D., 2022 · 2022
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Graph2route: A dynamic spatial-temporal graph neural network for pick-up and delivery route prediction, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4143–4152
Wen, H., Lin, Y., Mao, X., Wu, F., Zhao, Y., Wang, H., Zheng, J., Wu, L., Hu, H., Wan, H., 2022 · 2022
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A graph temporal information learning framework for popularity prediction, in: Companion Proceedings of the Web Conference 2022, pp. 239–242
Yang, C., Bao, P., Yan, R., Li, J., Li, X., 2022 · 2022
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Roland: graph learning framework for dynamic graphs, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2358–2366
You, J., Du, T., Leskovec, J., 2022 · 2022
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Instant graph neural networks for dynamic graphs, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2605–2615
Zheng, Y., Wang, H., Wei, Z., Liu, J., Wang, S., 2022 · 2022
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Papers and patents are becoming less disruptive over time
Park, M., Leahey, E., Funk, R.J., 2023 · 2023
Closest in time.
Re-examining lexical and semantic attention: Dual-view graph convolutions enhanced bert for academic paper rating
Xue, Z., He, G., Liu, J., Jiang, Z., Zhao, S., Lu, W., 2023 · 2023
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