The DOI matches its Crossref record; 32 of the 36 references it cites were found in a registry or an index, and 4 are not checked yet.
ArnetMiner: extraction and mining of academic social networks
Abstract
This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.
Checked 2026-11-10 · 4 references not checked yet
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Baeza-Yates, R. A., & Ribeiro-Neto, B. A. (1999) · Cited by 11,505
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243
Probabilistic Latent Semantic Indexing
Hofmann, T. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130370 · Cited by 3,988
Probabilistic latent semantic indexing
Hofmann, T. (1999) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/312624.312649 · Cited by 3,975
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Supervised Topic ModelsPreprint
Blei, D., & McAuliffe, J. D. (2007) · Neural Information Processing Systems · doi:10.48550/arxiv.1003.0783 · Cited by 1,860
Research works printed but not linked: 2
- N. Craswell , A. P. de Vries , and I. Soboroff . Overview of the trec-2005 enterprise track . In TREC'05 , pages 199 -- 205 , 2005 . N. Craswell, A. P. de Vries, and I. Soboroff. Overview of the trec-2005 enterprise track. In TREC'05, pages 199--205, 2005.
- A. McCallum . Multi-label text classification with a mixture model trained by em . In Proc. of AAAI'99 Workshop , 1999 . A. McCallum. Multi-label text classification with a mixture model trained by em. In Proc. of AAAI'99 Workshop, 1999.
Cited by
Sorted by citations
Items 1,501–1,600 of 2,231
Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization
Wang, S., Yang, X., Islam, R., Chen, H., Xu, M., Li, J., & Cai, Y. (2024) · Proceedings · doi:10.1109/icdm59182.2024.00108 · Cited by 3
Self-citationSIGRL: Sociologically-Informed Graph Representation Learning for Social Influence Prediction
Xu, H., Gao, C., Li, X., & Wang, Z. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3569545 · Cited by 3
Privacy-Preserving Social Network Integration, Analysis, and Mining
Yang, C. C. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-404702-0.00003-3 · Cited by 3
Connectivity in Complex Networks
Chen, C. (2018) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3159652.3170460 · Cited by 3
A framework for integrating bibliographical data of computer science publications
Do, T., Lam, D., & Huynh, T. V. (2014) · International Conference on Computing, Management and Telecommunications · doi:10.1109/commantel.2014.6825612 · Cited by 3
An Ontology Based Model for Experts Search and Ranking
Uddin, M. N., Duong, T. H., Oh, K.-J., & Jo, G. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20042-7_16 · Cited by 3
Understanding mass cooperation through visualization
Cazabet, R., & Takeda, H. (2014) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2631775.2631818 · Cited by 3
A semantic query-based approach for management decision-making
Shi, H., Chong, D., Yan, G., & He, W. (2015) · Journal of Management Analytics · doi:10.1080/23270012.2014.1003151 · Cited by 3
Liu, J., Liu, D., Yan, X., Dong, L., Zeng, T., Zhang, Y., & Tang, J. (2014) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2661829.2661852 · Cited by 3
Self-citationIndividual Doctor Recommendation in Large Networks by Constrained Optimization
Gong, J., Cheng, H., & Wang, L. (2015) · International Journal of Web Services Research · doi:10.4018/ijwsr.2015100102 · Cited by 3
k-Consistent Influencers in Network Data
Xu, E., Hsu, W., Lee, M. L., & Patel, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18123-3_27 · Cited by 3
Indirect weighted association rules mining for academic network collaboration recommendations
Koh, Y. S., & Dobbie, G. (2012) · Australasian Data Mining Conference · Cited by 3
A Dynamic Nonparametric Model for Characterizing the Topical Communities in Social Streams
Liu, Z., Zheng, Q., Wang, F., Tian, Z., & Li, B. (2014) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611973440.44 · Cited by 3
Spatial Context-Aware Recommendation
Yin, H., & Cui, B. (2016) · SpringerBriefs in computer science · doi:10.1007/978-981-10-0748-4_3 · Cited by 3
AdaWIRL: A Novel Bayesian Ranking Approach for Personal Big-Hit Paper Prediction
Zhang, C., Yu, L., Lu, J., Zhou, T., & Zhang, Z. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39958-4_27 · Cited by 3
Predicting the Impact of Software Engineering Topics
Sarkar, S., Lakdawala, R., & Datta, S. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3053051 · Cited by 3
Tajeuna, E. G., Bouguessa, M., & Wang, S. (2017) · ACM Symposium on Applied Computing · doi:10.1145/3019612.3019817 · Cited by 3
Influence Spread in Social Networks with both Positive and Negative Influences
He, J., Xie, Y., Du, T., Ji, S., & Li, Z. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-62389-4_51 · Cited by 3
Generating Topics of Interests for Research Communities
Kumar, N., Utkoor, R., Appareddy, B. K. R., & Singh, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69179-4_34 · Cited by 3
Network Embedding via a Bi-Mode and Deep Neural Network Model
Fang, Y., Zhao, X. H., Tan, Z., & Xiao, W. (2018) · Symmetry · doi:10.3390/sym10050180 · Cited by 3
Du, W., Yu, S., Yang, M., Qu, Q., & Zhu, J. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3186928 · Cited by 3
Research Paper Recommender System with Serendipity Using Tweets vs. Diversification
Nishioka, C., Hauke, J., & Scherp, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34058-2_7 · Cited by 3
Liu, J., Ma, S., Hu, R., Hu, C., & Huai, J. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371861 · Cited by 3
Lee, K. H., & Kim, M. H. (2020) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3375399 · Cited by 3
Study on the evolution of hot topics in the urban development
Zhou, P., & Jiang, D. (2020) · Evolutionary Intelligence · doi:10.1007/s12065-020-00391-y · Cited by 3
A Metadata Application Profile to Structure a Scientific Database for Social Network Analysis (SNA)
Lopez-Acosta, A., García-Hernández, A., Vázquez-Reyes, S., & Mauricio-González, A. (2020) · International Conference in Software Engineering Research and Innovation (CONISOFT) · doi:10.1109/conisoft50191.2020.00038 · Cited by 3
Search and evaluation methods for class level information retrieval
Henrich, A., & Wegmann, M. (2021) · ACM Symposium on Applied Computing · doi:10.1145/3412841.3442092 · Cited by 3
Siersdorfer, S., Kemkes, P., Ackermann, H., & Zerr, S. (2015) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2806416.2806582 · Cited by 3
Integrating Higher-Order Features for Structural Role Discovery
Tian, Q., Zhang, W., Jiao, P., Zhong, K., Wu, N., & Pan, L. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-23902-1_19 · Cited by 3
Dynamics of Information Diffusion
Kim, M. (2015) · ANU Open Research (Australian National University) · doi:10.25911/5d70f241bce00 · Cited by 3
Betweenness Ordering Problem : An Efficient Non-Uniform Sampling Technique for Large Graphs.Preprint
Agarwal, M., Singh, R. R., Chaudhary, S., & Iyengar, S. R. S. (2014) · arXiv (Cornell University) · Cited by 3
Spectral Network Embedding: A Fast and Scalable Method via SparsityPreprint
Zhang, J., Wang, Y., Tang, J., & Ding, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.02623 · Cited by 3
Self-citationCustomized Graph Embedding: Tailoring Embedding Vectors to different ApplicationsPreprint
Hou, B., Wang, Y., Zeng, M., Jiang, S., Mengshoel, O. J., Tong, Y., & Bai, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.09454 · Cited by 3
Simplifying Impact Prediction for Scientific ArticlesPreprint
Vergoulis, T., Kanellos, I., Giannopoulos, G., & Dalamagas, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15192 · Cited by 3
Lifelong Learning of Graph Neural Networks for Open-World Node Classification
Galke, L. P. A., Franke, B., Zielke, T., & Scherp, A. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3764769 · Cited by 3
Generating Local Maps of Science using Deep Bibliographic CouplingPreprint
Candel, G., & Naccache, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.10007 · Cited by 3
Context-aware recommender system for system of information systems
Li, S. (2021) · theses.fr (ABES) · doi:10.70675/8185b83azd062z45efz8727z0f066b58e4c7 · Cited by 3
Dataset artefacts can partially drive the measured decline in disruption
Holst, V., Algaba, A., Tori, F., Wenmackers, S., & Ginis, V. (2026) · Nature · doi:10.1038/s41586-026-10787-y · Cited by 3
Semantic Mining of Social Networks
Tang, J., & Li, J. (2015) · Synthesis lectures on data, semantics and knowledge · doi:10.1007/978-3-031-79462-9 · Cited by 2
Self-citationThe Design and Implement of Transmitter Remote Control System Based on the Embed Web Server
Xu, S.-P., Chen-yan, L., Su, X.-H., & Bai, X.-J. (2010) · International Conference on Multimedia Technology (ICMT) · doi:10.1109/icmult.2010.5630985 · Cited by 2
Bibliometric-Enhanced Information Retrieval: 8th International BIR Workshop
Cabanac, G., Frommholz, I., & Mayr, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-15719-7_55 · Cited by 2
Exploring Triangle-Free Dense Structures
Lu, C., Yu, J. X., & Wei, H. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2764468 · Cited by 2
Integrating researchers’ scientific production information through Ogmios
Verdugo, N., Guzmán, E., & Urdiales, C. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01479-8 · Cited by 2
HGIM: Influence maximization in diffusion cascades from the perspective of heterogeneous graph
Wang, Y., Zheng, Y., & Liu, Y. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04711-4 · Cited by 2
Rcoco: contrastive collective link prediction across multiplex network in Riemannian space
Sun, L., Li, M., Yang, Y., Li, X., Liu, L., Zhang, P., & Du, H. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02118-2 · Cited by 2
Disentangled Representation Learning for Structural Role Discovery
Zhang, W., Pan, L., Guo, X., & Jiao, P. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3404975 · Cited by 2
Explainable decomposition of nested dense subgraphs
Tatti, N. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-024-01053-8 · Cited by 2
Graph Mining under Data scarcity
Rakaraddi, A., Siew-Kei, L., Pratama, M., & de Carvalho, M. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651061 · Cited by 2
Fu, J., Guo, X., Hou, J., Yu, W., Shi, H., & Zhao, Y. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02261-w · Cited by 2
MC-GNN: Multi-Channel Graph Neural Networks With Hilbert-Schmidt Independence Criterion
Cui, S., Li, D., & Zhang, J. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3522817 · Cited by 2
Self-citationDisentangled hyperbolic representation learning for heterogeneous graphs
Bai, Q., Nie, C., Zhang, H., Dou, Z., & Yuan, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.112976 · Cited by 2
Graph Anomaly Detection via Multi-Scale Reconstruction of Graph Encoder-Decoder Networks
Yu, J., Yang, H. B., & Zhang, P. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888224 · Cited by 2
Li, Y., Zhu, Q., Song, F., Zheng, C., & Li, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890752 · Cited by 2
Self-citationRobust Neural Model for Searching over Incomplete Graphs
Rad, R. H., Bagheri, E., Kargar, M., Srivastava, D., & Szlichta, J. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3735650 · Cited by 2
AffinityTune: A Prompt-Tuning Framework for Few-Shot Anomaly Detection on Graphs
Chen, J., Zhu, G., Pang, G., Yuan, C., & Huang, Y. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736836 · Cited by 2
Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
Zhang, C., & Jung, J.-W. (2025) · Applied Sciences · doi:10.3390/app15158691 · Cited by 2
Extensible and Robust Evaluation of Similarity Queries
Schmitt, D., Hütter, T., & Augsten, N. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3749646.3749660 · Cited by 2
Structure-Attribute Transformations with Markov Chain Boost Graph Domain Adaptation
Liu, Z., Zhang, Y., Ren, S., & You, Y. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761141 · Cited by 2
Non-Dissipative Graph Propagation for Non-Local Community Detection
Leeney, W., Gravina, A., & Bacciu, D. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228363 · Cited by 2
Attacking Community Detection With Bounded Neighborhood Expansion and Edge Augmentation
Hsu, B.-Y., Lu, C.-H., Chang, Y., Chang, M., & Shen, C.-Y. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3637867 · Cited by 2
Dynamic homophily with imperfect recall: modeling resilience in adversarial networks
Alqithami, S. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01483-2 · Cited by 2
Liu, C., Tang, D., Lv, B., Zhang, P., & Huang, Z. (2026) · Scientometrics · doi:10.1007/s11192-025-05518-5 · Cited by 2
Enhance Before Fusion: Multi-View Graph Clustering With Graph Trend Filter
Wang, P., Lu, J., Wu, D., Wang, R., & Nie, F. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3655829 · Cited by 2
Domanetska, I., & Khrollenko, Y. (2025) · Management of Development of Complex Systems · doi:10.32347/2412-9933.2025.64.194-200 · Cited by 2
Mao, X., Li, J., Zhu, X., & Wang, Y. (2026) · International Review of Economics & Finance · doi:10.1016/j.iref.2026.105217 · Cited by 2
Self-citationA high-efficiency 3D-stacked accelerator for deep graph neural network inference
Long, Z., Zhang, Y., Yutao, F. U., Niu, Q., Lv, S., Zhai, J., Zhao, W., Yingqi, Z. H. A. O., Jin, Z. H. A. O., & Jin, H. (2026) · Scientia Sinica Informationis · doi:10.1360/ssi-2025-0381 · Cited by 2
FairHGNN: toward label-aware fairness in Heterogeneous Graph Neural Networks
Liu, Y., Wang, X., Zhu, C., Chang, L., & Gu, T. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-026-02786-2 · Cited by 2
Datasets for recommender systems: A survey and comparative overview
Polatidis, N., Baimagambetov, A., Kehagias, D. D., Trovati, M., Sarigiannidis, P. G., & Manolopoulos, Y. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01068-1 · Cited by 2
Li, X., Zhang, W., Zhang, Y., Jia, P., Wang, Y., Wang, Y., Liu, Y., Guo, H., & Zhao, X. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3808609 · Cited by 2
A Generalized Approach for Social Network Integration and Analysis with Privacy Preservation
Yang, C., & Thuraisingham, B. M. (2013) · Studies in big data · doi:10.1007/978-3-642-40837-3_8 · Cited by 2
Leveraging Multiple Networks for Author Personalization.
Parimi, R., & Caragea, D. (2015) · National Conference on Artificial Intelligence · Cited by 2
Long, J., Zhu, L., Yang, Z., Zhang, C., & Yuan, X. (2018) · Advances in Multimedia · doi:10.1155/2018/2058670 · Cited by 2
Annotated Corpus of Scientific Conference’s Homepages for Information Extraction
Andruszkiewicz, P., & Hazan, R. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/59kzdmrqnwa3 · Cited by 2
Team Expansion in Collaborative Environments
Zhao, L., Yao, Y., Guo, G., Tong, H., Xu, F., & Lü, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93040-4_56 · Cited by 2
A Data-Driven Unified Framework for Predicting Citation Dynamics
Γώγογλου, Α., & Manolopoulos, Y. (2018) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2884505 · Cited by 2
Pichardo-Corpus, J. A., Contreras, J. G., & de la Peña, J. A. (2018) · Journal of Complex Networks · doi:10.1093/comnet/cny037 · Cited by 2
Declarative User Selection with Soft Constraints
Amsterdamer, Y., Milo, T., Somech, A., & Youngmann, B. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358025 · Cited by 2
Person Name Recognition with Fine-grained Annotation
Dai, Y., Zhang, R., & Qi, J. (2020) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3383583.3398515 · Cited by 2
Unsupervised evaluation of multiple node ranks by reconstructing local structures
Krasanakis, E., Papadopoulos, S., & Kompatsiaris, I. Y. (2020) · Applied Network Science · doi:10.1007/s41109-020-00287-x · Cited by 2
Qu, L., Zhu, H., & Shi, Y. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185532 · Cited by 2
IGAEM: Improved Genetic Algorithm based Entity Matching
Aassem, Y., Hafidi, I., & Aboutabit, N. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1743/1/012001 · Cited by 2
An Ego Network Embedding Model via Neighbors Sampling and Self-attention Mechanism
Guo, Z., Liu, S., Pan, L., & He, Q. (2020) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00079 · Cited by 2
Author Homepage Discovery in CiteSeerX
Patel, K., Caragea, C., Caragea, D., & Giles, C. L. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i17.17778 · Cited by 2
Automatic View Selection in Graph Databases
Zhang, C., Lu, J., Guo, Q., Zhang, X., Han, X., & Zhou, M. (2021) · International Conference on Scientific and Statistical Database Management · doi:10.1145/3468791.3468794 · Cited by 2
PSOBER: PSO based entity resolution
Aassem, Y., Hafidi, I., Khalfi, H., & Aboutabit, N. (2021) · Mathematical Modeling and Computing · doi:10.23939/mmc2021.04.573 · Cited by 2
Comprehensively Computing Link-based Similarities by Building A Random Surfer Graph
Zhang, M., Yan, X., & Wang, W. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482329 · Cited by 2
A Novel Approach on Conducting Reviewer Recommendations Based on Conflict of Interest
Nugroho, A. S., Iswafaza, A. F., Anggraini, R. N. E., & Sarno, R. (2021) · International Conference on Information & Communication Technology and System (ICTS) · doi:10.1109/icts52701.2021.9609054 · Cited by 2
Cross Network Representation Matching with Outliers
Hou, M., Ren, J. F., Febrinanto, F. G., Shehzad, A. H., & Xia, F. (2021) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw53433.2021.00124 · Cited by 2
Semantic enhanced Top-k similarity search on weighted HIN
Zhang, Y., Yu, M., Zhang, T., & Yu, G. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07339-6 · Cited by 2
Random Walk on Multiple Networks
Luo, D., Bian, Y., Yan, Y., Yu, X. B., Huan, J., Liu, X., & Zhang, X. D. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3221668 · Cited by 2
Fair Link Prediction with Multi-Armed Bandit Algorithms
Wang, W., & Soundarajan, S. (2023) · ACM Web Science Conference (WebSci) · doi:10.1145/3578503.3583624 · Cited by 2
Azad, S., Hoque, M. A., Rimon, N. A., Habib, M. M. S., Mahmud, M., Kaiser, M. S., & Karim, M. R. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9483-8_42 · Cited by 2
Dynamic End-to-End Information Cascade Prediction Based on Neural Networks and Snapshot Capture
Han, D., Meng, T., & Li, M. (2023) · Electronics · doi:10.3390/electronics12132875 · Cited by 2
Liberate Pseudo Labels from Over-Dependence: Label Information Migration on Sparsely Labeled Graphs
Hu, Z., Fu, Y., Zhao, H., Cai, X., Jiang, W., & Pu, S. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614954 · Cited by 2
Silva, V. S., da Silva, J. V., Dias, T. M. R., Junior, R. F. G., & Segundo, W. L. R. D. C. (2023) · RDBCI Revista Digital de Biblioteconomia e Ciência da Informação · doi:10.20396/rdbci.v21i00.8673171 · Cited by 2
Layer imbalance-aware multiplex network embedding
Chen, K., Qiu, Y., Liu, Z., & Mu, W. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02072-z · Cited by 2
Multi-Interest Learning for Multi-Modal Paper Recommendation
Shen, X., Su, L., Xiao, X., & Li, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446181 · Cited by 2
Fairness in Graph-based Recommendation: Methods Overview
Čutura, L., Vladimir, K., Delač, G., & Šilić, M. (2024) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro60963.2024.10569148 · Cited by 2
Shamgunova, M., Biloshchytskyi, A. O., Kuchanskyi, O., & Faizullin, A. (2024) · IEEE International Conference on Smart Information Systems and Technologies (SIST) · doi:10.1109/sist61555.2024.10629492 · Cited by 2
Dense Subgraph Discovery Meets Strong Triadic Closure
Arachchi, C. W., Kumpulainen, I., & Tatti, N. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671697 · Cited by 2
Metadata
- Authors
- Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z.
- Affiliations
- Tsinghua University (CN)
- IBM Research (China) (CN)
- Venue
- Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2008-08-24
- Pages
- 990-998
- OpenAlex
- W2022322548
- Semantic Scholar
- f020b61789112fe7241b871907268f0bdc5c84fa
- Cited by
- 2,231
- References
- 33
- Citations by year
- 2026: 126 · 2025: 147 · 2024: 146 · 2023: 166 · 2022: 148 · 2021: 207 · 2020: 211 · 2019: 192 · 2018: 164 · 2017: 131 · 2016: 122 · 2015: 108 · 2014: 113 · 2013: 81 · 2012: 63
- FWCI
- 203.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Topics
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
- Keywords
- academic social networks, digital libraries, name ambiguity, network modeling, author co-citation networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A second model labelled each claim on 2026-11-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
