Graph-based Interference Models
Graph-Based Interference Models is a formal framework that represents interference effects through graph structures, where nodes denote entities or variables and edges encode potential causal or statistical dependencies that mediate interference, distinguishing it from traditional, non-network-based interference models by explicitly leveraging graph-theoretic representations to capture complex, structured interference patterns.
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2017–2026
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Peking University
Beijing, China
3 h-index
8 publications
642 citations
University of Michigan
Ann Arbor, United States
2 h-index
4 publications
94 citations
Georgia State University
Atlanta, United States
2 h-index
4 publications
94 citations

Huazhong University of Science and Technology
Wuhan, China
2 h-index
2 publications
47 citations
Tsinghua University
Beijing, China
2 h-index
6 publications
441 citations

Heilongjiang University
Harbin, China
2 h-index
2 publications
47 citations
The Ohio State University
Columbus, United States
2 h-index
6 publications
174 citations
Heilongjiang University of Science and Technology
Harbin, China
2 h-index
2 publications
47 citations
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4 publications · 243 citations
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TON · 3 publications · 107 citations
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Conference · 2 publications · 47 citations
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