The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Link prediction in weighted networks: The role of weak ties
Abstract
Plenty of algorithms for link prediction have been proposed and were applied to various real networks. Among these algorithms, the weights of links are rarely taken into account. In this letter, we use local similarity indices to estimate the likelihood of the existence of links in weighted networks, including Common Neighbor , Adamic-Adar Index , Resource Allocation Index , and their weighted versions. We have tested the prediction accuracy on real social, technological and biological networks. Overall speaking, the resource allocation index performs best. To our surprise, sometimes the weighted indices perform even worse than the unweighted indices, which reminds us of the well-known Weak-Ties Theory . Further experimental study shows that the weak ties play a significant role in the link prediction, and to emphasize the contributions of weak ties can remarkably enhance the prediction accuracy for some networks. We give a semi-quantitative explanation based on the motif analysis. This letter provides a start point for the possible weak-ties theory in information retrieval.
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
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Granovetter, M. S. (1973) · American Journal of Sociology · doi:10.1086/225469 · Cited by 40,491
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
Network motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
The architecture of complex weighted networks
Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400087101 · Cited by 4,218
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
The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer
Levin, D. Z., & Cross, R. (2004) · Management Science · doi:10.1287/mnsc.1030.0136 · Cited by 2,734
Cited by
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
Link prediction based on local random walk
Liu, W., & Lü, L. (2010) · EPL (Europhysics Letters) · doi:10.1209/0295-5075/89/58007 · Cited by 572
Self-citationAn epidemic model of rumor diffusion in online social networks
Cheng, J.-J., Liu, Y., Shen, B., & Yuan, W.-G. (2013) · The European Physical Journal B · doi:10.1140/epjb/e2012-30483-5 · Cited by 90
Metadata
- Authors
- Lü, L., & Zhou, T.
- Venue
- EPL (Europhysics Letters)
- Publisher
- IOP Publishing
- Published
- 2010-01-01
- Volume
- 89
- Issue
- 1
- Pages
- 18001
- ISSN
- 0295-5075 (ISSN-L)
- 1286-4854
- OpenAlex
- W2074436399
- Cited by
- 320
- References
- 29
- FWCI
- 10.86
- Type
- journal article
- 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: Link prediction in weighted networks: The role of weak ties. https://app.cochat.ai/papers/10.1209/0295-5075/89/18001
- The paper
- Lü, L., & Zhou, T. (2010). Link prediction in weighted networks: The role of weak ties. EPL (Europhysics Letters). https://doi.org/10.1209/0295-5075/89/18001
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.
