18 citations · 38 across the 6 of their papers we have counts for
6 papers
Using Motif Transitions for Temporal Graph Generation
Penghang Liu, A. Erdem Sarıyüce
Graph generative models are highly important for sharing surrogate data and benchmarking purposes. Real-world complex systems often exhibit dynamic nature, where the interactions a…
Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms
Penghang Liu, Bahadir Altun, Rupam Acharyya +4
Understanding the dynamics of financial transactions among people is critical for various applications such as fraud detection. One important aspect of financial transaction networ…
Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets
Penghang Liu, Kshama Dwarakanath, Svitlana S Vyetrenko +1
Human decision-making in real-life deviates significantly from the optimal decisions made by fully rational agents, primarily due to computational limitations or psychological bias…
Temporal Motifs in Patent Opposition and Collaboration Networks
Penghang Liu, Naoki Masuda, Tomomi Kito +1
Patents are intellectual properties that reflect innovative activities of companies and organizations. The literature is rich with the studies that analyze the citations among the…
Characterizing and Utilizing the Interplay Between Core and Truss Decompositions
Penghang Liu, A. Erdem Sarıyüce
Finding the dense regions in a graph is an important problem in network analysis. Core decomposition and truss decomposition address this problem from two different perspectives. T…
Temporal Network Motifs: Models, Limitations, Evaluation
Penghang Liu, Valerio Guarrasi, A. Erdem Sarıyüce
Investigating the frequency and distribution of small subgraphs with a few nodes/edges, i.e., motifs, is an effective analysis method for static networks. Motif-driven analysis is…