Simple and Deep Graph Convolutional Networks
- Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li
- Computer ScienceInternational Conference on Machine Learning
- 4 July 2020
The GCNII is proposed, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping} that effectively relieves the problem of over-smoothing.
Finding Top-k Min-Cost Connected Trees in Databases
- Bolin Ding, J. Yu, Shan Wang, Lu Qin, Xiao Zhang, Xuemin Lin
- Computer ScienceIEEE International Conference on Data Engineering
- 15 April 2007
This paper proposes a novel parameterized solution, with l as a parameter, to find the optimal GST-1, in time complexity O(3ln + 2l ((l + logn)n + m), where n and m are the numbers of nodes and edges in graph G, which can handle graphs with a large number of nodes.
Swarm: Mining Relaxed Temporal Moving Object Clusters
- Z. Li, Bolin Ding, Jiawei Han, R. Kays
- Computer ScienceProceedings of the VLDB Endowment
- 1 September 2010
In ObjectGrowth, two effective pruning strategies are proposed to greatly reduce the search space and a novel closure checking rule is developed to report closed swarms on-the-fly.
Distance-Constraint Reachability Computation in Uncertain Graphs
- R. Jin, Lin Liu, Bolin Ding, Haixun Wang
- Mathematics, Computer ScienceProceedings of the VLDB Endowment
- 1 June 2011
Driven by the emerging network applications, querying and mining uncertain graphs has become increasingly important. In this paper, we investigate a fundamental problem concerning uncertain graphs,…
Collecting Telemetry Data Privately
- Bolin Ding, Janardhan Kulkarni, S. Yekhanin
- Computer ScienceNIPS
- 4 December 2017
This paper develops new LDP mechanisms geared towards repeated collection of counter data, with formal privacy guarantees even after being executed for an arbitrarily long period of time, which have been deployed by Microsoft to collect telemetry across millions of devices.
Contrastive Learning for Sequential Recommendation
A novel multi-task framework called Contrastive Learning for Sequential Recommendation (CL4SRec) is proposed, which not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals from the original user behavior sequences.
Scalable Graph Neural Networks via Bidirectional Propagation
- Ming Chen, Zhewei Wei, Ji-rong Wen
- Computer ScienceNeural Information Processing Systems
- 29 October 2020
GBP is the first method that achieves sub-linear time complexity for both the precomputation and the training phases and can deliver superior performance on a graph with over 60 million nodes and 1.8 billion edges in less than half an hour on a single machine.
TwigList : Make Twig Pattern Matching Fast
- Lu Qin, J. Yu, Bolin Ding
- Computer ScienceInternational Conference on Database Systems for…
- 9 April 2007
A new algorithm, called TwigList, which uses simple lists, which significantly outperforms the up-to-date algorithm and both time and space complexity of the algorithm are linear with respect to the total number of pattern occurrences and the size of XML tree.
Online mobile Micro-Task Allocation in spatial crowdsourcing
- Yongxin Tong, Jieying She, Bolin Ding, Libin Wang, Lei Chen
- Computer ScienceIEEE International Conference on Data Engineering
- 16 May 2016
This paper identifies a more practical micro-task allocation problem, called the Global Online Micro-task Allocation in spatial crowdsourcing (GOMA) problem, and proposes a two-phase-based framework, based on which the TGOA algorithm with 1 over 4 -competitive ratio under the online random order model is presented.
Efficient Mining of Closed Repetitive Gapped Subsequences from a Sequence Database
- Bolin Ding, D. Lo, Jiawei Han, Siau-Cheng Khoo
- Computer ScienceIEEE International Conference on Data Engineering
- 29 March 2009
This paper introduces the concept of repetitive support to measure how frequently a pattern repeats in the database, and proposes efficient mining algorithms to find the complete set of desired patterns based on the idea of instance growth.
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