The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
Influence analysis in social networks: A survey
Open accessAbstract
Abstract Complementary to the fancy applications of social networks, influence analysis is an indispensable technique supporting these practical applications. In recent years, this emerging research branch has obtained significant attention from both industry and academia. In this new territory, researchers are facing many unprecedented theoretical and practical challenges. Thus, in this survey, we aim to pave a comprehensive and solid starting ground for interested readers by soliciting the latest work in this area. Firstly, we provide an overview of social networks, including definition, and types of social networks. Secondly, we present the current understanding of social influence analysis from different levels, such as its definition, properties, architecture, applications, and diffusion models. Thirdly, we discuss the evaluation metrics for social influence. Fourthly, we summarize the existing evaluation models on social influence in social networks. We further provide an overview of the existing methods for influence maximization. Finally, we discuss the problems of current algorithms and future trends from various perspectives in this field. We hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising research field.
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
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Rayward-Smith, V. J. (1991) · Journal of the Operational Research Society · doi:10.1057/jors.1991.155 · Cited by 16,821
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Authoritative sources in a hyperlinked environment
Kleinberg, J. M. (1999) · Journal of the ACM · doi:10.1145/324133.324140 · Cited by 8,959
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
Threshold models of collective behavior
Granovetter, M. (1978) · American Journal of Sociology · doi:10.1086/226707 · Cited by 6,072
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691
Node centrality in weighted networks: Generalizing degree and shortest paths
Opsahl, T., Agneessens, F., & Skvoretz, J. (2010) · Social Networks · doi:10.1016/j.socnet.2010.03.006 · Cited by 3,718
Borgatti, S. P. (2005) · Social Networks · doi:10.1016/j.socnet.2004.11.008 · Cited by 3,191
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022
Metadata
- Authors
- Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W.
- Venue
- Journal of Network and Computer Applications
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 106
- Pages
- 17-32
- ISSN
- 1084-8045 (ISSN-L)
- OpenAlex
- W2792550408
- Cited by
- 308
- References
- 98
- FWCI
- 16.19
- Type
- journal article
- Language
- en
- Open access
- green
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: Influence analysis in social networks: A survey. https://app.cochat.ai/papers/10.1016/j.jnca.2018.01.005
- The paper
- Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W. (2018). Influence analysis in social networks: A survey. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2018.01.005
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.