Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Modeling relationship strength in online social networks
2010429 citationsRongjing Xiang, Jennifer Neville et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Jennifer Neville
Since
Specialization
Citations
This map shows the geographic impact of Jennifer Neville's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jennifer Neville with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jennifer Neville more than expected).
Fields of papers citing papers by Jennifer Neville
This network shows the impact of papers produced by Jennifer Neville. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jennifer Neville. The network helps show where Jennifer Neville may publish in the future.
Co-authorship network of co-authors of Jennifer Neville
This figure shows the co-authorship network connecting the top 25 collaborators of Jennifer Neville.
A scholar is included among the top collaborators of Jennifer Neville based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Jennifer Neville. Jennifer Neville is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Eldardiry, Hoda, Jennifer Neville, & Ryan A. Rossi. (2020). Ensemble Learning for Relational Data. Journal of Machine Learning Research. 21(49). 1–37.
3.
Rao, Vinayak, et al.. (2019). A Stein–Papangelou Goodness-of-Fit Test for Point Processes. International Conference on Artificial Intelligence and Statistics. 226–235.2 indexed citations
4.
Neville, Jennifer, et al.. (2019). Social Reinforcement Learning to Combat Fake News Spread. Uncertainty in Artificial Intelligence. 1006–1016.7 indexed citations
Rao, Vinayak, et al.. (2018). Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy. International Conference on Machine Learning. 5561–5570.11 indexed citations
7.
Tan, Xi, Vinayak Rao, & Jennifer Neville. (2018). Nested CRP with Hawkes-Gaussian Processes. International Conference on Artificial Intelligence and Statistics. 1289–1298.2 indexed citations
8.
Tan, Xi, Vinayak Rao, & Jennifer Neville. (2018). The Indian Buffet Hawkes Process to Model Evolving Latent Influences. Uncertainty in Artificial Intelligence. 795–804.2 indexed citations
9.
Rao, Vinayak, et al.. (2017). Decoupling Homophily and Reciprocity with Latent Space Network Models.. Uncertainty in Artificial Intelligence.5 indexed citations
Pfeiffer, Joseph J., Jennifer Neville, & Paul N. Bennett. (2012). Active Sampling of Networks.8 indexed citations
12.
Nagaraj, Karthik, Charles Killian, & Jennifer Neville. (2012). Structured comparative analysis of systems logs to diagnose performance problems. Purdue e-Pubs (Purdue University System). 26–26.167 indexed citations
Xiang, Rongjing & Jennifer Neville. (2011). Relational Learning with One Network: An Asymptotic Analysis. International Conference on Artificial Intelligence and Statistics. 779–788.19 indexed citations
15.
Neville, Jennifer, et al.. (2011). Relational Active Learning for Joint Collective Classification Models. International Conference on Machine Learning. 385–392.21 indexed citations
Neville, Jennifer. (2004). Naked before God: Uncovering the Body in Anglo-Saxon England. 6.13 indexed citations
19.
Jensen, David, Jennifer Neville, & Michael Hay. (2003). Avoiding bias when aggregating relational data with degree disparity. International Conference on Machine Learning. 274–281.22 indexed citations
20.
Jensen, David & Jennifer Neville. (2002). Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning. International Conference on Machine Learning. 259–266.111 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.