In the original formulation of influence diagrams, each model contained exacly one utility node. Tatman and Shachter (1990) introduced the possibility of having super-value nodes that represent the sum or the product of their parents’ utility functions. However the algorithm they proposed for dealing with super-value nodes has two shortcomings: it requires dividing potentials when reversing arc...

In this paper, we introduce super Cayley graphs, a class of communication-efficient networks for parallel processing. We show that super Cayley graphs can embed trees, meshes, hypercubes, as well as star, bubble-sort graphs, and transposition networks with constant dilation. We also show that algorithms developed for star graphs can be emulated on suitably constructed super Cayley graphs with a...

Super-resolution techniques have addressed many biological questions, yet molecular quantification at rapid timescales in live tissues remains challenging. We developed a light microscopy system capable of sub-millisecond sampling to characterize molecular diffusion in heterogeneous aqueous environments comparable to interstitial regions between cells in tissues. We demonstrate our technique wi...

We propose a method to discover a different kind of influential nodes in a social network, which we call “super-mediators”, i.e., those nodes which play an important role in receiving the information and passing it to other nodes. We mathematically formulate this as a difference maximization problem in the average influence degree with respect to a node removal, i.e., a node that contributes to...

Maintaining user connectivity over heterogeneous wireless networks will be a necessity with the wide spread of wireless networks and limited geographic coverage and capacity of each network. In [1], we propose a super node system architecture based on the concept of delay tolerant network (DTN) to overcome roaming user intermittent connections over interconnected heterogeneous wireless networks...

Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network to be partitioned into a smaller network of ‘super nodes’, each super node comprising one or more nodes in the original network. To define the seeds of our super nodes, we apply the ‘CoreHD...

The ability to handle large scale graph data is crucial to an increasing number of applications. Much work has been dedicated to supporting basic graph operations such as subgraph matching, reachability, regular expression matching, etc. In many cases, graph indices are employed to speed up query processing. Typically, most indices require either super-linear indexing time or super-linear index...

A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the inherent efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilitie...

Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of smallmolecule based drug discovery. Molecules can be represented as an undirected graph, and we can utilize graph convolution networks to predication molecular properties. However, graph convolutional networks and other graph neural networks all focus on learning node-level representat...

SimRank is a well-studied similarity measure between two nodes in a network. However, evaluating SimRank of all nodes in a network is not only time-consuming but also not pragmatic, since users are only interested in the most similar pairs in many real-world applications. This paper focuses on topk similarity join based on SimRank. In this work, we first present an incremental algorithm for com...