The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
New perspectives and methods in link prediction
Abstract
This paper examines important factors for link prediction in networks and provides a general, high-performance framework for the prediction task. Link prediction in sparse networks presents a significant challenge due to the inherent disproportion of links that can form to links that do form. Previous research has typically approached this as an unsupervised problem. While this is not the first work to explore supervised learning, many factors significant in influencing and guiding classification remain unexplored. In this paper, we consider these factors by first motivating the use of a supervised framework through a careful investigation of issues such as network observational period, generality of existing methods, variance reduction, topological causes and degrees of imbalance, and sampling approaches. We also present an effective flow-based predicting algorithm, offer formal bounds on imbalance in sparse network link prediction, and employ an evaluation method appropriate for the observed imbalance. Our careful consideration of the above issues ultimately leads to a completely general framework that outperforms unsupervised link prediction methods by more than 30% AUC.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Self-citationData Mining: Practical Machine Learning Tools and Techniques
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,437
(1992) · Cited by 23,431
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Witten, I. H. (2008) · Cited by 9,143
A New Status Index Derived from Sociometric Analysis
Katz, L. (1953) · Psychometrika · doi:10.1007/bf02289026 · Cited by 3,866
The link‐prediction problem for social networks
Liben‐Nowell, D., & Kleinberg, J. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20591 · Cited by 3,043
Friends and neighbors on the Web
Adamic, L. A., & Adar, E. (2003) · Social Networks · doi:10.1016/s0378-8733(03)00009-1 · Cited by 2,954
Clustering and preferential attachment in growing networks
Newman, M. E. J. (2001) · Physical Review E · doi:10.1103/physreve.64.025102 · Cited by 2,093
Cited by
A survey of heterogeneous information network analysis
Shi, C., Li, Y., Zhang, J., Sun, Y., & Yu, P. S. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2598561 · Cited by 1,112
A Survey of Link Prediction in Complex Networks
Martínez, V., Berzal, F., & Cubero, J.-C. (2017) · ACM Computing Surveys · doi:10.1145/3012704 · Cited by 695
Human mobility, social ties, and link prediction
Wang, D., Pedreschi, D., Song, C., Giannotti, F., & Barabasi, A.-L. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020581 · Cited by 665
Link prediction in social networks: the state-of-the-art
Wang, P., Xu, B., Wu, Y., & Zhou, X. (2015) · Science China Information Sciences · doi:10.1007/s11432-014-5237-y · Cited by 548
Exploiting place features in link prediction on location-based social networks
Scellato, S., Noulas, A., & Mascolo, C. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020575 · Cited by 522
Metadata
- Authors
- Lichtenwalter, R. N., Lussier, J. T., & Chawla, N. V.
- Venue
- Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2010-07-25
- Pages
- 243-252
- OpenAlex
- W2003707464
- Cited by
- 664
- References
- 19
- FWCI
- 24.27
- Type
- conference paper
- Language
- en
- Open access
- closed
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.
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: New perspectives and methods in link prediction. https://app.cochat.ai/papers/10.1145/1835804.1835837
- The paper
- Lichtenwalter, R. N., Lussier, J. T., & Chawla, N. V. (2010). New perspectives and methods in link prediction. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1835804.1835837
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
