Maximizing Activity in Ising Networks via the TAP Approximation

A wide array of complex biological, social, and physical systems have recently been shown to be quantitatively described by Ising models, which lie at the intersection of statistical physics and machine learning. Here, we study the fundamental question of how to optimize the state of a networked Ising system given a budget of external influence. In the continuous setting where one can tune the influence applied to each node, we propose a series of approximate gradient ascent algorithms based on the Plefka expansion, which generalizes the na\"{i}ve mean field and TAP approximations. In the discrete setting where one chooses a small set of influential nodes, the problem is equivalent to the famous influence maximization problem in social networks with an additional stochastic noise term. In this case, we provide sufficient conditions for when the objective is submodular, allowing a greedy algorithm to achieve an approximation ratio of $1-1/e$. Additionally, we compare the Ising-based algorithms with traditional influence maximization algorithms, demonstrating the practical importance of accurately modeling stochastic fluctuations in the system

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters

Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE). With 5G in view, the number of these COPs are expected to reach 2000 per site, making their manual tuning for finding the optimal combination of these parameters, an impossible fleet. Alongside these thousands of COPs is the anticipated network densification in emerging networks which exacerbates the burden of the network operators in managing and optimizing the network. Hence, we propose a machine learning-based framework combined with a heuristic technique to discover the optimal combination of two pertinent COPs used in mobility, Cell Individual Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio (SINR) of all the connected users. The first part of the framework leverages the power of machine learning to predict the KPI of interest given several different combinations of CIO and HOM. The resulting predictions are then fed into Genetic Algorithm (GA) which searches for the best combination of the two mentioned parameters that yield the maximum mean SINR for all users. Performance of the framework is also evaluated using several machine learning techniques, with CatBoost algorithm yielding the best prediction performance. Meanwhile, GA is able to reveal the optimal parameter setting combination more efficiently and with three orders of magnitude faster convergence time in comparison to brute force approach

A survey of QoS-aware web service composition techniques

Web service composition can be briefly described as the process of aggregating services with disparate functionalities into a new composite service in order to meet increasingly complex needs of users. Service composition process has been accurate on dealing with services having disparate functionalities, however, over the years the number of web services in particular that exhibit similar functionalities and varying Quality of Service (QoS) has significantly increased. As such, the problem becomes how to select appropriate web services such that the QoS of the resulting composite service is maximized or, in some cases, minimized. This constitutes an NP-hard problem as it is complicated and difficult to solve. In this paper, a discussion of concepts of web service composition and a holistic review of current service composition techniques proposed in literature is presented. Our review spans several publications in the field that can serve as a road map for future research

Finding influential users for different time bounds in social networks using multi-objective optimization

Online social networks play an important role in marketing services. Influence maximization is a major challenge, in which the goal is to find the most influential users in a social network. Increasing the number of influenced users at the end of a diffusion process while decreasing the time of diffusion are two main objectives of the influence maximization problem. The goal of this paper is to find multiple sets of influential users such that each of them is the best set to spread influence for a specific time bound. Considering two conflicting objectives, increasing influence and decreasing diffusion time, we employ the NSGA-II algorithm which is a powerful algorithm in multi-objective optimization to find different seed sets with high influence at different diffusion times. Since social networks are large, computing influence and diffusion time of all chromosomes in each iteration will be challenging and computationally expensive. Therefore, we propose two methods which can estimate the expected influence and diffusion time of a seed set in an efficient manner. Providing the set of all potentially optimal solutions helps a decision maker evaluate the trade-offs between the two objectives, i.e., the number of influenced users and diffusion time. In addition, we develop an approach for selecting seed sets, which have optimal influence for specific time bounds, from the resulting Pareto front of the NSGA-II. Finally, we show that applying our algorithm to real social networks outperforms existing algorithms for the influence maximization problem. The results show a good compromise between the two objectives and the final seed sets result in high influence for different time bounds