Integer programming approaches to the multiple team formation problem

Identifying the most influential individuals can provide invaluable help in developing and deploying effective viral marketing strategies. Previous studies mainly focus on designing efficient algorithms or heuristics to find top-K influential nodes on a given static social network. While, as a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time, significantly affecting the efficiency. In this paper, we observe from real-world traces that the evolution of social network follows the preferential attachment rule and the influential nodes are mainly selected from high-degree nodes. Such observations shed light on the design of IncInf, an incremental approach that can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to effectively narrow the search space into nodes experiencing major increases or with high degrees. We carried out extensive experiments on real-world dynamic social networks including Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static heuristic, IncInf achieves as much as 21\(\times \) speedup in execution time while maintaining matching performance in terms of influence spread.

Background Online social networks share similar topological characteristics as real-world social networks. Many studies have been conducted to analyze the online social networks, but it is difficult to link human interests with social network software design. Purpose The goal of this work is to propose a methodology involving the analysis of human interactions for use in designing online social network software. Methods We propose a novel use of social network analysis techniques to elicit requirements in order to design better online Social network-based software. The validation case study involved the collection of real-world data by means of a questionnaire to perform a network design construction and analysis. The key idea is to examine social network to help in the identification of behaviors and interests of people for better software requirements elicitation. Results The validation case study demonstrates how unexpected centrality measures can emerge in real world networks. Our case study can thus conducted as a baseline for better requirement elicitation studies for online social network software design. This work also indicates how sociometric methods may be used to analyze any social domain as a possible standard practice in online social network software design. Overall, the study proved the effectiveness of the proposed novel methodology for the design of online social network software. The methodology specifically improves upon traditional methods for software design by involving social network modeling and analysis to first study the behavior and elicit requirements to develop more resilient online social network sites.

This paper presents a technique for memory optimization for a class of computations that arises in the field of correlated electronic structure methods such as coupled cluster and configuration interaction methods in quantum chemistry. In this class of computations, loop computations perform a multi-dimensional sum of product of input arrays. There are many different ways to get the same final results that differ in the required number of arithmetic operations required. In addition, for a given number of arithmetic operations, different expressions of the loop have different memory requirements. Loop fusion is a plausible solution for reducing memory usage. By fusing loops between producer loop nest and consumer loop nest, the required storage of intermediate array is reduced by the range of the fused loop. Because resultant loops have to be legal after fusion, some loops can not be fused at the same time. In this paper, we have developed a novel integer linear programming (ILP) formulation that is shown to be highly effective on a number of test cases producing the optimal solutions using very small execution times. The main idea in the ILP formulation is the encoding of legality rules for loop fusion of a special class of loops using logical constraints over binary decision variables and a highly effective approximation of memory usage

This paper considers two-dimensional (2-D) retiming, which is the problem of retiming circuits that operate on 2-D signals. We begin by discussing two types of parallelism available in 2-D data processing, which we call inter-iteration parallelism and inter-operation parallelism. We then present two novel techniques for 2-D retiming that can be used to extract inter-operation parallelism. These two techniques are designed to minimize the amount of memory required to implement a 2-D data-flow graph while maintaining a desired clock rate for the circuit. The first technique is based on an integer linear programming (ILP) formulation of the problem, and is called ILP 2-D retiming. This technique considers the entire 2-D retiming problem as a whole, but long central processing unit times are required if the circuit is large. The second technique, called orthogonal 2-D retiming, is a linear programming formulation which is derived by partitioning ILP 2-D retiming into two parts called s- and a-retiming. This technique finds a solution in polynomial time and is much faster than the ILP 2-D retiming technique, but the two sub problems (s- and a-retiming) can give results which are not compatible with one another. To solve this incompatibility problem, a variation of orthogonal 2-D retiming called integer orthogonal 2-D retiming is developed. This technique runs in polynomial time and the s-retiming and a-retiming steps are guaranteed to give compatible results. We show that the techniques presented in this paper can result in memory hardware savings of 50% compared to previously published 2-D retiming techniques.

This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key "seed" nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ~160% in terms of information spread about HIV among homeless youth.

Summary form only given. We present a way to automatically select, within an architectural synthesis tool, the best operand and operator number systems, in order to find the best speed/area tradeoff. This implies the use of different number systems (redundant and non-redundant) for the same design: this is what we call mixed arithmetics. We present an integer linear programming (ILP) formulation to solve a scheduling problem.

In general, multicast routing and wavelength assignment (MC-RWA) can be subdivided in routing and wavelength assignment issues in wavelength-division multiplexing (WDM) mesh networks. Previous studies on WDM multicast have mainly focused on WDM multicast routing. The multicast wavelength assignment problem is studied in this paper. A unicast routing path can be established by a lightpath in an all-optical network. However, in the multicasting case, a multicast routing tree can be established by a single light-tree or several lightpaths, or a combination of several light-trees and lightpaths. We propose a wavelength assignment algorithm for finding an optimal combination of lightpaths and light-trees to construct a newly required multicast session. First of all, two cost functions are given to evaluate the establishing cost for each feasible wavelength, and then find a set of wavelengths that covers all destinations with the minimal cost using Integer Linear Programming (ILP) formulation. We focus on maximizing the total number of users served in a multicast session and the network capacity. The simulation results show that the proposed algorithm can improve system resource utilization and reduce the blocking probability compared with the First-Fit algorithm.

Coarse-grained reconfigurable array (CGRA) overlays can improve dataflow kernel throughput by an order of magnitude over Vivado HLS. This is possible with a combination of carefully floorplanned high-frequency (645-768 MHz) design and a scalable, communication-aware compiler. Our 2D torus CGRA architecture supports versatile processing element (PE) functionality and a configurable number of communication channels to match application demands. Compared to recent FPGA overlays like CGRA-ME's ADRES and HyCUBE implementations, our design operates at 1.8-3.4× faster clock frequency, while scaling to an orders-of-magnitude larger array size of 19×69 on Xilinx Alveo U280. Our communication-aware compiler targets HLS objectives such as initiation interval (II) and minimizes communication cost using an integer linear programming (ILP) formulation. Unlike SDC schedulers in FPGA HLS tools, we treat data movement as a first-class citizen by encoding the space and time resources communication network of the overlay in the ILP formulation. Given the same constraints on operational resources as Vivado HLS, we can retain our target II and achieve up to 9.2× higher frequency. Our ILP scheduler outperforms a PathFinder space-time router implementation in quality of result by up to nearly 2×.

The Fiber Installation Problem (FIP) in Wavelength Division Multiplexing (WDM) optical networks consists in routing a set of lightpaths (all-optical connections) such that the cost of the optical devices necessary to operate the network is minimized. Each of these devices is worth hundreds of thousands of dollars. In consequence, any improvement in the lightpath routing may save millions of dollars for the network operator. All the works in the literature for solving this problem are based on greedy heuristics and genetic algorithms. No information is known on how good are the solutions provided by these heuristics compared to the optimal solution. Besides, no proof that the problem is NP-Hard can be found. In this paper, we prove that FIP is NP-Hard and also present an Integer Linear Programming (ILP) formulation for the problem. In addition, we propose an implementation of the Iterated Local Search (ILS) metaheuristic to solve large instances of the problem. Computational experiments carried out on 21 realistic instances showed that the CPLEX solver running with our ILP formulation was able to solve 11 out of the 21 instances to optimality in less than two minutes. These results also showed that the ILS heuristic has an average optimality gap of 1% on the instances for which the optimal solution is known. For the other instances, the results showed that the proposed heuristic outperforms the best heuristic in the literature by 7%.

We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of new infections subject to a budget constraint on the total number of available vaccinations for the contagions. While this problem has been considered in the literature for a single contagion, our work considers the simultaneous propagation of two contagions. This optimization problem is NP-hard. We present two main solution approaches for the problem, namely an integer linear programming (ILP) formulation to obtain optimal solutions and a heuristic based on a generalization of the set cover problem. We carry out a comprehensive experimental evaluation of our solution approaches using many real-world networks. The experimental results show that our heuristic algorithm produces solutions that are close to the optimal solution and runs several orders of magnitude faster than the ILP-based approach for obtaining optimal solutions. We also carry out sensitivity studies of our heuristic algorithm.

An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks

A key challenge in the social network community is the problem of network generation -- that is, how can we create synthetic networks that match characteristics traditionally found in most real world networks? Important characteristics that are present in social networks include a power law degree distribution, small diameter, and large amounts of clustering. However, most current network generators, such as the Chung Lu and Kronecker models, largely ignore the clustering present in a graph and focus on preserving other network statistics, such as the power law distribution. Models such as the exponential random graph model have a transitivity parameter that can capture clustering, but they are computationally difficult to learn, making scaling to large real world networks intractable. In this work, we propose an extension to the Chung Lu random graph model, the Transitive Chung Lu (TCL) model, which incorporates the notion transitive edges. Specifically, it combines the standard Chung Lu model with edges that are formed through transitive closure (e.g., by connecting a 'friend of a friend'). We prove TCL's expected degree distribution is equal to the degree distribution of the original input graph, while still providing the ability to capture the clustering in the network. The single parameter required by our model can be learned in seconds on graphs with millions of edges, networks can be generated in time that is linear in the number of edges. We demonstrate the performance of TCL on four real-world social networks, including an email dataset with hundreds of thousands of nodes and millions of edges, showing TCL generates graphs that match the degree distribution, clustering coefficients and hop plots of the original networks.

Integer Linear Programming (ILP) is commonly used in high level and system level synthesis. It is an NP-Complete problem (in general cases). There exists some tools that give an optimal solution for small ILP formulations. Nevertheless, these tools may not give solutions for complex formulations. In this paper, we present a solution to overcome the problem of complexity in ILP formulations. We propose a partitioning methodology based on a constraint graph representing all the constraints included in any ILP formulation. To direct the partitioning, the constraint graph nodes are grouped to represent Data Flow Graph (DFG) nodes. This reduced constraint graph can be used to partition any ILP formulation based on DFG. We illustrate this method on ILP formulation for scheduling. Results on ILP scheduling formulations are promising.

Existing works on identity privacy protection on social networks make the assumption that all the user identities in a social network are private and ignore the fact that in many real-world social networks, there exists a considerable amount of users such as celebrities, media users, and organization users whose identities are public. In this paper, we demonstrate that the presence of public users can cause serious damage to the identity privacy of other ordinary users. Motivated attackers can utilize the connection information of a user to some known public users to perform re-identification attacks, namely connection fingerprint (CFP) attacks. We propose two k-anonymization algorithms to protect a social network against the CFP attacks. One algorithm is based on adding dummy vertices. It can resist powerful attackers with the connection information of a user with the public users within n hops (n ≥ 1) and protect the centrality utility of public users. The other algorithm is based on edge modification. It is only able to resist attackers with the connection information of a user with the public users within 1 hop but preserves a rich spectrum of network utility. We perform comprehensive experiments on real-world networks and demonstrate that our algorithms are very efficient in terms of the running time and are able to generate k-anonymized networks with good utility.

The Prize-collecting Scheduling Problem with Deadlines