Gennaro Cordasco

Latency-Bounded Target Set Selection in Social Networks

We study variants of the Target Set Selection problem, first proposed by Kempe et al. In our scenario one is given a graph G = (V,E), integer values t(v) for each vertex v, and the objective is to determine a small set of vertices (target set) that activates a given number (or a given subset) of vertices of G within a prescribed number of rounds. The activation process in G proceeds as follows: initially, at round 0, all vertices in the target set are activated; subsequently at each round r ≥ 1 every vertex of G becomes activated if at least t(v) of its neighbors are active by round r − 1. It is known that the problem of finding a minimum cardinality Target Set that eventually activates the whole graph G is hard to approximate to a factor better than \(O(2^{\log^{1-\epsilon }|V|})\). In this paper we give exact polynomial time algorithms to find minimum cardinality Target Sets in graphs of bounded clique-width, and exact linear time algorithms for trees.

Distributed Load Balancing for Parallel Agent-Based Simulations

We focus on agent-based simulations where a large number of agents move in the space, obeying to some simple rules. Since such kind of simulations are computational intensive, it is challenging, for such a contest, to let the number of agents to grow and to increase the quality of the simulation. A fascinating way to answer to this need is by exploiting parallel architectures. In this paper, we present a novel distributed load balancing schema for a parallel implementation of such simulations. The purpose of such schema is to achieve an high scalability. Our approach to load balancing is designed to be lightweight and totally distributed: the calculations for the balancing take place at each computational step, and in?uences the successive step. To the best of our knowledge, our approach is the ?rst distributed load balancing schema in this context. We present both the design and the implementation that allowed us to perform a number of experiments, with up-to 1, 000, 000 agents. Tests show that, in spite of the fact that the load balancing algorithm is local, the workload distribution is balanced while the communication overhead is negligible.

F-Chord: Improved Uniform Routing on Chord

We propose a family of novel schemes based on Chord retain- ing all positive aspects that made Chord a popular topology for routing in P2P networks. The schemes, based on the Fibonacci number system, allow to improve on the maximum/average number of hops for lookups and the routing table size per node.

Bringing together efficiency and effectiveness in distributed simulations: The experience with D-Mason

Agent-based simulation models are an increasingly popular tool for research and management in many fields. In executing such simulations “speed” is one of the most general and important issues because of the size and complexity of simulations. But another important issue is the effectiveness of the solution, which consists of how easily usable and portable the solutions are for the users, i.e. the programmers of the distributed simulation. Our study, then, is aimed at efficient and effective distribute simulations by adopting a framework-level approach, with our design and implementation of a framework, D-Mason, which is a parallel version of the Mason library for writing and running simulations of agent-based simulation models. In particular, besides the efficiency due to workload distribution with small overhead, D-Mason at a framework level proves itself effective since it enables the scientists that use the framework (domain expert but with limited knowledge of distributed programming) only minimally aware of the fact that the simulation is running on a distributed environment. Then, we present tests that compare D-Mason against Mason in order to assess the improved scalability and D-Mason capability to exploit heterogeneous distributed hardware. Our tests also show that several massive simulations that are impossible to execute on Mason (e.g. because of CPU and/or memory requirements) can be easily performed using D-Mason.

Community detection via semi-synchronous label propagation algorithms

A recently introduced novel community detection strategy is based on a label propagation algorithm (LPA) which uses the diffusion of information in the network to identify communities. Studies of LPAs showed that the strategy is effective in finding a good community structure. Label propagation step can be performed in parallel on all nodes (synchronous model) or sequentially (asynchronous model); both models present some drawback, e.g., algorithm termination is nor granted in the first case, performances can be worst in the second case. In this paper, we present a semi-synchronous version of LPA which aims to combine the advantages of both synchronous and asynchronous models. We prove that our models always converge to a stable labeling. Moreover, we experimentally investigate the effectiveness of the proposed strategy comparing its performance with the asynchronous model both in terms of quality, efficiency and stability. Tests show that the proposed protocol does not harm the quality of the partitioning. Moreover it is quite efficient; each propagation step is extremely parallelizable and it is more stable than the asynchronous model, thanks to the fact that only a small amount of randomization is used by our proposal.

2-Chord Halved

We present 2-Chord Halved, a distributed peer-to-peer lookup protocol. Our proposal is based on Chord exhibit the following advantages: i) We show a stabilization procedure that eliminates the fixfinger procedure of Chord protocol. Our strategy allows to inform each node on the ring that is interested to a topological change. Fixfinger in Chord costs O(log/sup 2/ N) messages when it is ran on all finger table entries even if the finger table is up to date, contrariwise our stabilization procedure, that has the same cost, is ran only if there are join or leave operations and only on the interested nodes. ii) We present a new strategy to implement the join/leave operations using the predecessors finger table of joined node and exploiting the fingers of predecessor as start point searching new fingers. This procedure costs O(logN log logN) w.h.p., contrariwise to Chord within join/leave operation cost O(log/sup 2/ N) w.h.p. iii) We show a new routing strategy that has a moderate improvement on average path length. The improvements are obtained with no harm to the operational efficiency (e.g. stability, scalability, fault-tolerance, node congestion) of the Chord systems.

On scheduling dag s for volatile computing platforms: Area-maximizing schedules

Many modern computing platforms-notably clouds and desktop grids-exhibit dynamic heterogeneity: the availability and computing power of their constituent resources can change unexpectedly and dynamically, even in the midst of a computation. We introduce a new quality metric, area, for schedules that execute computations having interdependent constituent chores (jobs, tasks, etc.) on such platforms. Area measures the average number of tasks that a schedule renders eligible for execution at each step of a computation. Even though the definition of area does not mention and properties of host platforms (such as volatility), intuition suggests that rendering tasks eligible at a faster rate will have a benign impact on the performance of volatile platforms-and we report on simulation experiments that support this intuition. We derive the basic properties of the area metric and show how to efficiently craft area-maximizing (A-M) schedules for several classes of significant computations. Simulations that compare A-M scheduling against heuristics ranging from lightweight ones (e.g., FIFO) to computationally intensive ones suggest that A-M schedules complete computations on volatile heterogeneous platforms faster than their competition, by percentages that vary with computation structure and platform behavior-but are often in the double digits.

Label propagation algorithm: a semi-synchronous approach

A recently introduced novel community detection strategy is based on a label propagation (LP) algorithm which uses the diffusion of information in the network to identify communities. Studies of LP algorithms showed that the strategy is effective in finding a good community structure. Label propagation step can be performed in parallel on all nodes (synchronous model) or sequentially (asynchronous model); both models present some drawback, e.g., algorithm termination is not granted in the first case, performances can be worst in the second case. In this paper, we present a semi-synchronous version of LP algorithms which aims to combine the advantages of both synchronous and asynchronous models. We prove that our models always converge to a stable labelling. Moreover, we experimentally investigate the effectiveness of the proposed strategy comparing its performance with the asynchronous model both in terms of quality, efficiency and stability. Tests show that the proposed protocol does not harm the quality of the partitioning. Moreover, it is quite efficient; each propagation step is extremely parallelisable and it is more stable than the asynchronous model, thanks to the fact that only a small amount of randomisation is used by our proposal.

Spread of influence in weighted networks under time and budget constraints

Given a network represented by a weighted directed graph G, we consider the problem of finding a bounded cost set of nodes S such that the influence spreading from S in G, within a given time bound, is as large as possible. The dynamic that governs the spread of influence is the following: initially only elements in S are influenced; subsequently at each round, the set of influenced elements is augmented by all nodes in the network that have a sufficiently large number of already influenced neighbors. We prove that the problem is NP-hard, even in simple networks like complete graphs and trees. We also derive a series of positive results. We present exact pseudo-polynomial time algorithms for general trees, that become polynomial time in case the trees are unweighted. This last result improves on previously published results. We also design polynomial time algorithms for general weighted paths and cycles, and for unweighted complete graphs.

EMOTHAW: A Novel Database for Emotional State Recognition From Handwriting and Drawing

The detection of negative emotions through daily activities such as writing and drawing is useful for promoting wellbeing. The spread of human–machine interfaces such as tablets makes the collection of handwriting and drawing samples easier. In this context, we present a first publicly available database which relates emotional states to handwriting and drawing, that we call EMOTHAW (EMOTion recognition from HAndWriting and draWing). This database includes samples of 129 participants whose emotional states, namely anxiety, depression, and stress, are assessed by the Depression–Anxiety–Stress Scales (DASS) questionnaire. Seven tasks are recorded through a digitizing tablet: pentagons and house drawing, words copied in handprint, circles and clock drawing, and one sentence copied in cursive writing. Records consist in pen positions, on-paper and in-air, time stamp, pressure, pen azimuth, and altitude. We report our analysis on this database. From collected data, we first compute measurements related to timing and ductus. We compute separate measurements according to the position of the writing device: on paper or in-air. We analyze and classify this set of measurements (referred to as features) using a random forest approach. This latter is a machine learning method [1], based on an ensemble of decision trees, which includes a feature ranking process. We use this ranking process to identify the features which best reveal a targeted emotional state. We then build random forest classifiers associated with each emotional state. We provide accuracy, sensitivity, and specificity evaluation measures obtained from cross-validation experiments. Our results show that anxiety and stress recognition perform better than depression recognition.