[Rescheduled] Seminar by Professor Chenlei Leng, Department of Statistics, University of Warwick, U.K.

Reciprocity—the tendency for directed edges to appear in mutual pairs—is a key feature of many networks but remains difficult to model, especially in sparse settings. This talk introduces two regression frameworks for capturing reciprocity in directed graphs with covariates. The first uses a novel Bernoulli formulation to distinguish reciprocal from non-reciprocal ties, along with an inference method and analysis of effective sample sizes under sparsity. The second extends the classical p1 model by allowing for link-specific reciprocity alongside node heterogeneity. We propose a new estimator based on a conditioning argument and provide theoretical guarantees. Simulations illustrate the models' performance and support the theory. This is based on joint work with Rui Feng.

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