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Uncovering User Interaction Dynamics in Online Social Networks.

, , , , and . ICWSM, page 698-701. AAAI Press, (2015)

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Unfolding dynamics in a social network: co-evolution oflink formation and user interaction., , , , , and . WWW (Companion Volume), page 67-68. International World Wide Web Conferences Steering Committee / ACM, (2013)Process-driven Analysis of Dynamics in Online Social Interactions., , , , and . COSN, page 139-149. ACM, (2015)Distributed Graph Computation Meets Machine Learning., , , , , , , and . IEEE Trans. Parallel Distributed Syst., 31 (7): 1588-1604 (2020)Dense-to-Sparse Gate for Mixture-of-Experts., , , , , , , , and . CoRR, (2021)FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement., , , , , , , and . Proc. ACM Manag. Data, 1 (1): 110:1-110:19 (2023)Optimizing Dynamic Neural Networks with Brainstorm., , , , , , , , , and 4 other author(s). OSDI, page 797-815. USENIX Association, (2023)Welder: Scheduling Deep Learning Memory Access via Tile-graph., , , , , , , , and . OSDI, page 701-718. USENIX Association, (2023)GraM: scaling graph computation to the trillions., , , , , , , , and . SoCC, page 408-421. ACM, (2015)Efficient Distributed Machine Learning with Trigger Driven Parallel Training., , , and . GLOBECOM, page 1-6. IEEE, (2016)A Topology Construct and Control Model with Small-World and Scale-Free Concepts for Heterogeneous Sensor Networks., , , and . IJDSN, (2014)