• Publications
  • Influence
Scalable Influence Estimation in Continuous-Time Diffusion Networks
Experiments on both synthetic and real-world data show that the proposed algorithm can easily scale up to networks of millions of nodes while significantly improves over previous state-of-the-arts in terms of the accuracy of the estimated influence and the quality of the selected nodes in maximizing the influence.
Joint Slot Filling and Intent Detection via Capsule Neural Networks
A capsule-based neural network model is proposed which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema and a re-routing schema is proposed to further synergize the slot filling performance using the inferred intent representation.
Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams
This new model establishes a previously unexplored connection between Bayesian Nonparametrics and temporal Point Processes, which makes the number of clusters grow to accommodate the increasing complexity of online streaming contents, while at the same time adapts to the ever changing dynamics of the respective continuous arrival time.
Learning Networks of Heterogeneous Influence
This paper proposes a kernel-based method which can capture a diverse range of different types of influence without any prior assumption and shows that this model can better recover the underlying diffusion network and drastically improve the estimation of the transmission functions among networked entities.
Uncover Topic-Sensitive Information Diffusion Networks
This paper proposes a continuous time model, TOPICCASCADE, for topicsensitive information diffusion networks, and infer the hidden diffusion networks and the topic dependent transmission rates from the observed time stamps and contents of cascades.
A Deep Learning Approach to Link Prediction in Dynamic Networks
A novel deep learning framework, i.e., Conditional Temporal Restricted Boltzmann Machine (ctRBM), which predicts links based on individual transition variance as well as influence introduced by local neighbors is proposed, which outperforms existing algorithms in link inference on dynamic networks.
Community detection in large-scale social networks
The algorithm ComTector (Community DeTector) is presented which is more efficient for the community detection in large-scale social networks based on the nature of overlapping communities in the real world and a general naming method is proposed by combining the topological information with the entity attributes to define the discovered communities.
Time-Sensitive Recommendation From Recurrent User Activities
This work proposes a novel framework which connects self-exciting point processes and low-rank models to capture the recurrent temporal patterns in a large collection of user-item consumption pairs and develops an efficient algorithm that maintains O(1/∊) convergence rate, scales up to problems with millions ofuser-item pairs and hundreds of millions of temporal events.
Improved recommendation based on collaborative tagging behaviors
A new collaborative filtering approach TBCF (Tag-based Collaborative Filtering) based on the semantic distance among tags assigned by different users to improve the effectiveness of neighbor selection, which has significant improvement against the traditional cosine-based recommendation method while leveraging user input not explicitly targeting the recommendation system.
A Version-aware Approach for Web Service Directory
  • Ru Fang, L. Lam, +4 authors Nan Du
  • Computer Science
    IEEE International Conference on Web Services…
  • 9 July 2007
This work proposes a version-aware service model based on some architectural extensions to WSDL and UDDI, enhanced to describe the attributes of the service versions, and designs a proxy, residing in the service consumer side which can dynamically update the client application instance at runtime.