This map shows the geographic impact of Tad Hogg'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 Tad Hogg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tad Hogg more than expected).
This network shows the impact of papers produced by Tad Hogg. 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 Tad Hogg. The network helps show where Tad Hogg may publish in the future.
Co-authorship network of co-authors of Tad Hogg
This figure shows the co-authorship network connecting the top 25 collaborators of Tad Hogg.
A scholar is included among the top collaborators of Tad Hogg 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 Tad Hogg. Tad Hogg is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hogg, Tad, Kristina Lerman, & Laura Mazzoli Smith. (2013). Stochastic Models Predict User Behavior in Social Media. arXiv (Cornell University). 2(1). 25–39.3 indexed citations
6.
Lerman, Kristina & Tad Hogg. (2009). Stochastic Models of Large-Scale Human Behavior on the Web. National Conference on Artificial Intelligence. 37–42.1 indexed citations
7.
Chen, Kay‐Yut & Tad Hogg. (2009). Modeling Risky Economic Decision-Making with Bounded Rationality.. National Conference on Artificial Intelligence. 1–6.2 indexed citations
8.
Hogg, Tad & Bernardo A. Huberman. (2008). Solving the Organizational Free Riding Problem with Social Networks. National Conference on Artificial Intelligence. 24–29.5 indexed citations
9.
Hogg, Tad, Dennis M. Wilkinson, Gábor Szabó, & Michael J. Brzozowski. (2008). Multiple Relationship Types in Online Communities and Social Networks.. National Conference on Artificial Intelligence. 30–35.34 indexed citations
Kubica, Jeremy, et al.. (2001). Agent-based control for object manipulation with modular self-reconfigurable robots. International Joint Conference on Artificial Intelligence. 1344–1349.7 indexed citations
12.
Hogg, Tad. (1998). Which search problems are random. National Conference on Artificial Intelligence. 438–443.5 indexed citations
13.
Hogg, Tad. (1997). Exploiting the deep structure of constraint satisfaction problems with quantum computers. National Conference on Artificial Intelligence. 334–339.1 indexed citations
14.
Clearwater, Scott H. & Tad Hogg. (1994). Exploiting problem structure in genetic algorithms. National Conference on Artificial Intelligence. 1310–1315.8 indexed citations
15.
Hogg, Tad & Colin P. Williams. (1994). Expected gains from parallelizing constraint solving for hard problems. National Conference on Artificial Intelligence. 331–336.22 indexed citations
16.
Williams, Colin P. & Tad Hogg. (1993). Extending deep structure. National Conference on Artificial Intelligence. 152–157.17 indexed citations
17.
Williams, Colin P. & Tad Hogg. (1992). Using deep structure to locate hard problems. National Conference on Artificial Intelligence. 472–477.43 indexed citations
18.
Kephart, Jeffrey O., Tad Hogg, & Bernardo A. Huberman. (1991). Collective behavior of predictive agents. MIT Press eBooks. 48–65.4 indexed citations
19.
Hogg, Tad & J. O. Kephart. (1990). Phase transitions in high-dimensional pattern classification. Computer Systems: Science & Engineering. 5(4). 223–232.4 indexed citations
20.
Hogg, Tad, et al.. (1987). A Dynamical Approach to Temporal Pattern Processing. Neural Information Processing Systems. 750–759.28 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.