Quantification and Comparison of Degree Distributions in Complex Networks

The degree distribution is an important characteristic of complex networks. In many applications, quantification of degree distribution in the form of a fixed-length feature vector is a necessary step. On the other hand, we often need to compare the degree distribution of two given networks and extract the amount of similarity between the two distributions. In this paper, we propose a novel method for quantification of the degree distributions in complex networks. Based on this quantification method,a new distance function is also proposed for degree distributions, which captures the differences in the overall structure of the two given distributions. The proposed method is able to effectively compare networks even with different scales, and outperforms the state of the art methods considerably, with respect to the accuracy of the distance function

Visual Search at eBay

In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure. We discuss the challenges we faced for a massive volatile inventory like at eBay and present our solution to overcome those. We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale. Supervised approach for optimized search limited to top predicted categories and also for compact binary signature are key to scale up without compromising accuracy and precision. Both use a common deep neural network requiring only a single forward inference. The system architecture is presented with in-depth discussions of its basic components and optimizations for a trade-off between search relevance and latency. This solution is currently deployed in a distributed cloud infrastructure and fuels visual search in eBay ShopBot and Close5. We show benchmark on ImageNet dataset on which our approach is faster and more accurate than several unsupervised baselines. We share our learnings with the hope that visual search becomes a first class citizen for all large scale search engines rather than an afterthought.Comment: To appear in 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017. A demonstration video can be found at https://youtu.be/iYtjs32vh4

Low Cost Quality of Service Multicast Routing in High Speed Networks

Many of the services envisaged for high speed networks, such as B-ISDN/ATM, will support real-time applications with large numbers of users. Examples of these types of application range from those used by closed groups, such as private video meetings or conferences, where all participants must be known to the sender, to applications used by open groups, such as video lectures, where partcipants need not be known by the sender. These types of application will require high volumes of network resources in addition to the real-time delay constraints on data delivery. For these reasons, several multicast routing heuristics have been proposed to support both interactive and distribution multimedia services, in high speed networks. The objective of such heuristics is to minimise the multicast tree cost while maintaining a real-time bound on delay. Previous evaluation work has compared the relative average performance of some of these heuristics and concludes that they are generally efficient, although some perform better for small multicast groups and others perform better for larger groups. Firstly, we present a detailed analysis and evaluation of some of these heuristics which illustrates that in some situations their average performance is reversed; a heuristic that in general produces efficient solutions for small multicasts may sometimes produce a more efficient solution for a particular large multicast, in a specific network. Also, in a limited number of cases using Dijkstra's algorithm produces the best result. We conclude that the efficiency of a heuristic solution depends on the topology of both the network and the multicast, and that it is difficult to predict. Because of this unpredictability we propose the integration of two heuristics with Dijkstra's shortest path tree algorithm to produce a hybrid that consistently generates efficient multicast solutions for all possible multicast groups in any network. These heuristics are based on Dijkstra's algorithm which maintains acceptable time complexity for the hybrid, and they rarely produce inefficient solutions for the same network/multicast. The resulting performance attained is generally good and in the rare worst cases is that of the shortest path tree. The performance of our hybrid is supported by our evaluation results. Secondly, we examine the stability of multicast trees where multicast group membership is dynamic. We conclude that, in general, the more efficient the solution of a heuristic is, the less stable the multicast tree will be as multicast group membership changes. For this reason, while the hybrid solution we propose might be suitable for use with closed user group multicasts, which are likely to be stable, we need a different approach for open user group multicasting, where group membership may be highly volatile. We propose an extension to an existing heuristic that ensures multicast tree stability where multicast group membership is dynamic. Although this extension decreases the efficiency of the heuristics solutions, its performance is significantly better than that of the worst case, a shortest path tree. Finally, we consider how we might apply the hybrid and the extended heuristic in current and future multicast routing protocols for the Internet and for ATM Networks.

Hierarchical Network Design Using Simulated Annealing

The hierarchical network problem is the problem of nding the least cost net-work, with nodes divided into groups, edges connecting nodes in each groups and groups ordered in a hierarchy. The idea of hierarchical networks comes from telecommunication networks where hierarchies exist. Hierarchical net-works are described and a mathematical model is proposed for a two level version of the hierarchical network problem. The problem is to determine which edges should connect nodes, and how demand is routed in the net-work. The problem is solved heuristically using simulated annealing which as a sub-algorithm uses a construction algorithm to determine edges and route the demand. Performance for dierent versions of the algorithm are reported in terms of runtime and quality of the solutions. The algorithm is able to nd solutions of reasonable quality in approximately 1 hour for networks with 100 nodes

Advanced Protocols for Peer-to-Peer Data Transmission in Wireless Gigabit Networks

This thesis tackles problems on IEEE 802.11 MAC layer, network layer and application layer, to further push the performance of wireless P2P applications in a holistic way. It contributes to the better understanding and utilization of two major IEEE 802.11 MAC features, frame aggregation and block acknowledgement, to the design and implementation of opportunistic networks on off-the-shelf hardware and proposes a document exchange protocol, including document recommendation. First, this thesis contributes a measurement study of the A-MPDU frame aggregation behavior of IEEE 802.11n in a real-world, multi-hop, indoor mesh testbed. Furthermore, this thesis presents MPDU payload adaptation (MPA) to utilize A-MPDU subframes to increase the overall throughput under bad channel conditions. MPA adapts the size of MAC protocol data units to channel conditions, to increase the throughput and lower the delay in error-prone channels. The results suggest that under erroneous conditions throughput can be maximized by limiting the MPDU size. As second major contribution, this thesis introduces Neighborhood-aware OPPortunistic networking on Smartphones (NOPPoS). NOPPoS creates an opportunistic, pocket-switched network using current generation, off-the-shelf mobile devices. As main novel feature, NOPPoS is highly responsive to node mobility due to periodic, low-energy scans of its environment, using Bluetooth Low Energy advertisements. The last major contribution is the Neighborhood Document Sharing (NDS) protocol. NDS enables users to discover and retrieve arbitrary documents shared by other users in their proximity, i.e. in the communication range of their IEEE 802.11 interface. However, IEEE 802.11 connections are only used on-demand during file transfers and indexing of files in the proximity of the user. Simulations show that NDS interconnects over 90 \% of all devices in communication range. Finally, NDS is extended by the content recommendation system User Preference-based Probability Spreading (UPPS), a graph-based approach. It integrates user-item scoring into a graph-based tag-aware item recommender system. UPPS utilizes novel formulas for affinity and similarity scoring, taking into account user-item preference in the mass diffusion of the recommender system. The presented results show that UPPS is a significant improvement to previous approaches