Collaborating Authors


Stacking Models for Nearly Optimal Link Prediction in Complex Networks Machine Learning

Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and improve the validity of network models. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 548 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity via meta-learning to construct a series of "stacked" models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are also superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state-of-the-art for link prediction comes from combining individual algorithms, which achieves nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvement of these results.

Intel Extends FPGA Ecosystem: Edge, Network, Data Center


The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of data center acceleration needs are concerned – has compelled IT managers and chip makers to venture out, increasingly, beyond CPUs and GPUs. The "inherent parallelism" of FPGAs (see below) to handle specialized workloads in AI- and HPDA-related implementations has brought on greater investments from IT decision makers and vendors, who see increasing justification for the challenge of FPGA programming. Of course, adoption of unfamiliar technologies is always painful and slow, particularly those without a built-out ecosystem of frameworks and APIs that simplify their use. Why are FPGAs bursting out of their communication, industrial and military niches and into the data center? Partly because of the limits of CPUs, which have their roots on the desktop and were, said Steve Conway, senior research VP at Hyperion Research, never really intended for advanced computing.

A bag-of-paths framework for network data analysis Machine Learning

This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. The central idea is to assign a probability distribution on the set of all paths in a network. More precisely, a Gibbs-Boltzmann distribution is defined over a bag of paths in a network, that is, on a representation that considers all paths independently. We show that, under this distribution, the probability of drawing a path connecting two nodes can easily be computed in closed form by simple matrix inversion. This probability captures a notion of relatedness between nodes of the graph: two nodes are considered as highly related when they are connected by many, preferably low-cost, paths. As an application, two families of distances between nodes are derived from the BoP probabilities. Interestingly, the second distance family interpolates between the shortest path distance and the resistance distance. In addition, it extends the Bellman-Ford formula for computing the shortest path distance in order to integrate sub-optimal paths by simply replacing the minimum operator by the soft minimum operator. Experimental results on semi-supervised classification show that both of the new distance families are competitive with other state-of-the-art approaches. In addition to the distance measures studied in this paper, the bag-of-paths framework enables straightforward computation of many other relevant network measures.