Abstract-In this paper we propose a new paradigm for a Differential Service (DiffServ) network consisting of two-color marking at the edges of the network using token buckets coupled with differential treatment in the core. Using fluid-flow modelling, we present existence conditions for tokenbucket rates and differential marking probabilities at the core that result in all edges receiving at least their minimum guaranteed rates. We then present an integrated DiffServ architecture comprising of an active rate management controller at the marking edge and a two-level active queue management controller at the core. The validity of the fluid flow model and performance of this new scheme are verified using ns simulations.

Abstract-The majority of influence maximization (IM) studies focus on targeting influential seeders to trigger substantial information spread in social networks. Motivated by the observation that incentives could "boost" users so that they are more likely to be influenced by friends, we consider a new and complementary k-boosting problem which aims at finding k users to boost so to trigger a maximized "boosted" influence spread. The kboosting problem is different from the IM problem because boosted users behave differently from seeders: boosted users are initially uninfluenced and we only increase their probability to be influenced. Our work also complements the IM studies because we focus on triggering larger influence spread on the basis of given seeders. Both the NP-hardness of the problem and the non-submodularity of the objective function pose challenges to the k-boosting problem. To tackle the problem on general graphs, we devise two efficient algorithms with the data-dependent approximation ratio. To tackle the problem on bidirected trees, we present an efficient greedy algorithm and a dynamic programming that is a fully polynomial-time approximation scheme. Extensive experiments using real social networks and synthetic bidirected trees verify the efficiency and effectiveness of the proposed algorithms. In particular, on general graphs, we show that boosting solutions returned by our algorithms achieves boosts of influence that are up to several times higher than those achieved by boosting intuitive solutions with no approximation guarantee. We also explore the "budget allocation" problem experimentally, demonstrating the beneficial of allocating the budget to both seeders and boosted users.

a b s t r a c tEnergy efficiency is one of the most important concerns in wireless networks because wireless clients usually have limited battery power. The aim of this work is to reduce energy consumption by exploiting multi-rate diversity in 802.11 wireless networks. An important observation is that ''probabilistic rate combination" in transmission can significantly reduce power consumption. We formulate the energy efficient rate combination as a non-convex optimization problem. A non-cooperative rate adaptation scheme is presented to reduce power consumption without information exchange. Each node selects rate combination strategy and computes its transmission probability based on the weighted average interface queue length. Due to the well-known ''rate anomaly" problem, selfish nodes may choose to transmit at a lower rate free ride from the other nodes. To mitigate this problem, we propose a joint consecutive packet transmission (CPT) and contention window adaptation mechanism (CWA). We prove the stability of our proposed algorithm, and to the best of our knowledge, this is the first control theoretical analysis on 802.11 ''multi-rate" wireless networks. Simulation results show that the probabilistic rate combination can greatly save battery power, even up to 700% times compared with standard 802.11a/h protocol.Crown

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Graphlet counting is a widely explored problem in network analysis and has been successfully applied to a variety of applications in many domains, most notatbly bioinformatics, social science, and infrastructure network studies. Efficiently computing graphlet counts remains challenging due to the combinatorial explosion, where a naive enumeration algorithm needs O(Nk) time for k-node graphlets in a network of size N. Recently, many works introduced carefully designed combinatorial and sampling methods with encouraging results. However, the existing methods ignore the fact that graphlet counts and the graph structural information are correlated. They always consider a graph as a new input and repeat the tedious counting procedure on a regular basis even if it is similar or exactly isomorphic to previously studied graphs. This provides an opportunity to speed up the graphlet count estimation procedure by exploiting this correlation via learning methods. In this paper, we raise a novel graphlet count learning (GCL) problem: given a set of historical graphs with known graphlet counts, how to learn to estimate/predict graphlet count for unseen graphs coming from the same (or similar) underlying distribution. We develop a deep learning framework which contains two convolutional neural network models and a series of data preprocessing techniques to solve the GCL problem. Extensive experiments are conducted on three types of synthetic random graphs and three types of real-world graphs for all 3-, 4-, and 5-node graphlets to demonstrate the accuracy, efficiency, and generalizability of our framework. Compared with state-of-the-art exact/sampling methods, our framework shows great potential, which can offer up to two orders of magnitude speedup on synthetic graphs and achieve on par speed on real-world graphs with competitive accuracy.

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