scholarly journals TemporalRI: subgraph isomorphism in temporal networks with multiple contacts

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Giovanni Micale ◽  
Giorgio Locicero ◽  
Alfredo Pulvirenti ◽  
Alfredo Ferro
Keyword(s):  
State Of The Art ◽  
Search Space ◽  
The State ◽  
Subgraph Isomorphism ◽  
Temporal Network ◽  
Temporal Networks ◽  
Chronological Order ◽  
Multiple Contacts ◽  
Ri Algorithm ◽  
Target Matching

AbstractTemporal networks are graphs where each edge is associated with a timestamp denoting when two nodes interact. Temporal Subgraph Isomorphism (TSI) aims at retrieving all the subgraphs of a temporal network (called target) matching a smaller temporal network (called query), such that matched target edges appear in the same chronological order of corresponding query edges. Few algorithms have been proposed to solve the TSI problem (or variants of it) and most of them are applicable only to small or specific queries. In this paper we present TemporalRI, a new subgraph isomorphism algorithm for temporal networks with multiple contacts between nodes, which is inspired by RI algorithm. TemporalRI introduces the notion of temporal flows and uses them to filter the search space of candidate nodes for the matching. Our algorithm can handle queries of any size and any topology. Experiments on real networks of different sizes show that TemporalRI is very efficient compared to the state-of-the-art, especially for large queries and targets.

scholarly journals Particle Swarm Contour Search Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 407 ◽  
Author(s):  
Dominik Weikert ◽  
Sebastian Mai ◽  
Sanaz Mostaghim
Keyword(s):  
Image Processing ◽  
Real World ◽  
State Of The Art ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Local Information ◽  
The State ◽  
Complete Knowledge ◽  
Real World Applications

In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.

scholarly journals Temporal network alignment via GoT-WAVE

2019 ◽  
Vol 35 (18) ◽  
pp. 3527-3529 ◽  
Author(s):  
David Aparício ◽  
Pedro Ribeiro ◽  
Tijana Milenković ◽  
Fernando Silva
Keyword(s):  
User Interface ◽  
State Of The Art ◽  
Source Code ◽  
Network Alignment ◽  
Supplementary Information ◽  
Temporal Network ◽  
Temporal Networks ◽  
Supplementary Data ◽  
Node Similarity ◽  
User Friendly

Abstract Motivation Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results On synthetic networks, GoT-WAVE improves DynaWAVE’s accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE. Availability and implementation http://www.dcc.fc.up.pt/got-wave/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Speeding Up Incomplete GDL-based Algorithms for Multi-agent Optimization with Dense Local Utilities

2020 ◽  
Author(s):  
Yanchen Deng ◽  
Bo An
Keyword(s):  
Utility Function ◽  
Branch And Bound ◽  
State Of The Art ◽  
Search Space ◽  
Computational Effort ◽  
The State ◽  
Computational Overhead ◽  
Multi Agent

Incomplete GDL-based algorithms including Max-sum and its variants are important methods for multi-agent optimization. However, they face a significant scalability challenge as the computational overhead grows exponentially with respect to the arity of each utility function. Generic Domain Pruning (GDP) technique reduces the computational effort by performing a one-shot pruning to filter out suboptimal entries. Unfortunately, GDP could perform poorly when dealing with dense local utilities and ties which widely exist in many domains. In this paper, we present several novel sorting-based acceleration algorithms by alleviating the effect of densely distributed local utilities. Specifically, instead of one-shot pruning in GDP, we propose to integrate both search and pruning to iteratively reduce the search space. Besides, we cope with the utility ties by organizing the search space of tied utilities into AND/OR trees to enable branch-and-bound. Finally, we propose a discretization mechanism to offer a tradeoff between the reconstruction overhead and the pruning efficiency. We demonstrate the superiorities of our algorithms over the state-of-the-art from both theoretical and experimental perspectives.

Evaluating Top-k Skyline Queries Efficiently

2011 ◽  
pp. 102-117
Author(s):  
Marlene Goncalves ◽  
María Esther Vidal
Keyword(s):  
State Of The Art ◽  
Search Space ◽  
Large Datasets ◽  
The State ◽  
Experimental Results ◽  
High Dimensional ◽  
Skyline Queries ◽  
Speed Up

Criteria that induce a Skyline naturally represent user’s preference conditions useful to discard irrelevant data in large datasets. However, in the presence of high-dimensional Skyline spaces, the size of the Skyline can still be very large. To identify the best k points among the Skyline, the Top-k Skyline approach has been proposed. This chapter describes existing solutions and proposes to use the TKSI algorithm for the Top-k Skyline problem. TKSI reduces the search space by computing only a subset of the Skyline that is required to produce the top-k objects. In addition, the Skyline Frequency Metric is implemented to discriminate among the Skyline objects those that best meet the multidimensional criteria. This chapter’s authors have empirically studied the quality of TKSI, and their experimental results show the TKSI may be able to speed up the computation of the Top-k Skyline in at least 50% percent with regard to the state-of-the-art solutions.

Fast Strong Planning for FOND Problems with Multi-Root Directed Acyclic Graphs

2014 ◽  
Vol 23 (06) ◽  
pp. 1460028 ◽  
Author(s):  
Andres Calderon Jaramillo ◽  
Jicheng Fu ◽  
Vincent Ng ◽  
Farokh B. Bastani ◽  
I-Ling Yen
Keyword(s):  
State Of The Art ◽  
Solution Space ◽  
Strong Solutions ◽  
Search Space ◽  
Directed Acyclic Graphs ◽  
The State ◽  
Benchmark Problems ◽  
Acyclic Graphs ◽  
Planning Problems ◽  
Improve Planning

Recently, the state-of-the-art AI planners have significantly improved planning efficiency on Fully Observable Nondeterministic planning (FOND) problems with strong cyclic solutions. These strong cyclic solutions are guaranteed to achieve the goal if they terminate, implying that there is a possibility that they may run into indefinite loops. In contrast, strong solutions are guaranteed to achieve the goal, but few planners can effectively handle FOND problems with strong solutions. In this study, we aim to address this difficult, yet under-investigated class of planning problems: FOND planning problems with strong solutions. We present a planner that employs a new data structure, MRDAG (multi-root directed acyclic graph), to define how the solution space should be expanded. Based on the characteristics of MRDAG, we develop heuristics to ensure planning towards the relevant search direction and design optimizations to prune the search space to further improve planning efficiency. We perform extensive experiments to evaluate MRDAG, the heuristics, and the optimizations for pruning the search space. Experimental results show that our strong algorithm achieves impressive performance on a variety of benchmark problems: on average it runs more than three orders of magnitude faster than the state-of-the-art planners, MBP and Gamer, while demonstrating significantly better scalability.

scholarly journals Causal Paths in Temporal Networks of Face-to-Face Human Interactions

2021 ◽  
Vol 30 (1) ◽  
pp. 33-46
Author(s):  
Agostino Funel ◽  
Keyword(s):  
Social Contexts ◽  
Temporal Network ◽  
Temporal Networks ◽  
Chronological Order ◽  
Face To Face ◽  
Average Value ◽  
Human Interactions ◽  
A Value ◽  
Time Correlations ◽  
Time Gap

In a temporal network, causal paths are characterized by the fact that links from a source to a target must respect the chronological order. In this paper we study the causal paths structure in temporal networks of face-to-face human interactions in different social contexts. In a static network, paths are transitive; that is, the existence of a link from a to b and from b to c implies the existence of a path from a to c via b. In a temporal network, the chronological constraint introduces time correlations that affect transitivity. A probabilistic model based on higher-order Markov chains shows that correlations that can invalidate transitivity are present only when the time gap between consecutive events is larger than the average value and are negligible below such a value. The comparison between the densities of the temporal and static accessibility matrices shows that the static representation can be used with good approximation. Moreover, we quantify the extent of the causally connected region of the networks over time.

scholarly journals Exploiting Partial Assignments for Efficient Evaluation of Answer Set Programs with External Source Access

2018 ◽  
Vol 62 ◽  
pp. 665-727 ◽  
Author(s):  
Thomas Eiter ◽  
Tobias Kaminski ◽  
Christoph Redl ◽  
Antonius Weinzierl
Keyword(s):  
State Of The Art ◽  
Search Space ◽  
External Source ◽  
The State ◽  
Efficiency Gain ◽  
Model Search ◽  
Wide Range ◽  
Search Space Pruning ◽  
Answer Sets ◽  
Answer Set

Answer Set Programming (ASP) is a well-known declarative problem solving approach based on nonmonotonic logic programs, which has been successfully applied to a wide range of applications in artificial intelligence and beyond. To address the needs of modern applications, HEX-programs were introduced as an extension of ASP with external atoms for accessing information outside programs via an API style bi-directional interface mechanism. To evaluate such programs, conflict-driving learning algorithms for SAT and ASP solving have been extended in order to capture the semantics of external atoms. However, a drawback of the state-of-the-art approach is that external atoms are only evaluated under complete assignments (i.e., input to the external source) while in practice, their values often can be determined already based on partial assignments alone (i.e., from incomplete input to the external source). This prevents early backtracking in case of conflicts, and hinders more efficient evaluation of HEX-programs. We thus extend the notion of external atoms to allow for three-valued evaluation under partial assignments, while the two-valued semantics of the overall HEX-formalism remains unchanged. This paves the way for three enhancements: first, to evaluate external sources at any point during model search, which can trigger learning knowledge about the source behavior and/or early backtracking in the spirit of theory propagation in SAT modulo theories (SMT). Second, to optimize the knowledge learned in terms of so-called nogoods, which roughly speaking are impossible input-output configurations. Shrinking nogoods to their relevant input part leads to more effective search space pruning. And third, to make a necessary minimality check of candidate answer sets more efficient by exploiting early external evaluation calls. As this check usually accounts for a large share of the total runtime, optimization is here particularly important. We further present an experimental evaluation of an implementation of a novel HEX-algorithm that incorporates these enhancements using a benchmark suite. Our results demonstrate a clear efficiency gain over the state-of-the-art HEX-solver for the benchmarks, and provide insights regarding the most effective combinations of solver configurations.

scholarly journals Neural Architecture Search of SPD Manifold Networks

2021 ◽  
Author(s):  
Rhea Sanjay Sukthanker ◽  
Zhiwu Huang ◽  
Suryansh Kumar ◽  
Erik Goron Endsjo ◽  
Yan Wu ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Statistical Evaluation ◽  
State Of The Art ◽  
Search Space ◽  
Positive Definite ◽  
The State ◽  
Cell Design ◽  
Training Process ◽  
Neural Architecture ◽  
Symmetric Positive Definite

In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks, aiming to automate the design of SPD neural architectures. To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design. Further, we model our new NAS problem with a one-shot training process of a single supernet. Based on the supernet modeling, we exploit a differentiable NAS algorithm on our relaxed continuous search space for SPD neural architecture search. Statistical evaluation of our method on drone, action, and emotion recognition tasks mostly provides better results than the state-of-the-art SPD networks and traditional NAS algorithms. Empirical results show that our algorithm excels in discovering better performing SPD network design and provides models that are more than three times lighter than searched by the state-of-the-art NAS algorithms.

scholarly journals ODSS: Efficient Hybridization for Optimal Coalition Structure Generation

2020 ◽  
Vol 34 (05) ◽  
pp. 7079-7086
Author(s):  
Narayan Changder ◽  
Samir Aknine ◽  
Sarvapali Ramchurn ◽  
Animesh Dutta
Keyword(s):  
Hybrid Algorithm ◽  
State Of The Art ◽  
Coalition Structure ◽  
Search Space ◽  
The State ◽  
Structure Generation ◽  
Complete Problem ◽  
Disjoint Sets ◽  
Np Complete ◽  
Coalition Structure Generation

Coalition Structure Generation (CSG) is an NP-complete problem that remains difficult to solve on account of its complexity. In this paper, we propose an efficient hybrid algorithm for optimal coalition structure generation called ODSS. ODSS is a hybrid version of two previously established algorithms IDP (Rahwan and Jennings 2008) and IP (Rahwan et al. 2009). ODSS minimizes the overlapping between IDP and IP by dividing the whole search space of CSG into two disjoint sets of subspaces and proposes a novel subspace shrinking technique to reduce the size of the subspace searched by IP with the help of IDP. When compared to the state-of-the-art against a wide variety of value distributions, ODSS is shown to perform better by up to 54.15% on benchmark inputs.

scholarly journals Improved CP-Based Lagrangian Relaxation Approach with an Application to the TSP

2021 ◽  
Author(s):  
Raphaël Boudreault ◽  
Claude-Guy Quimper
Keyword(s):  
Lagrangian Relaxation ◽  
State Of The Art ◽  
Optimization Technique ◽  
Search Space ◽  
The State ◽  
Traveling Salesman ◽  
Resolution Time ◽  
Lagrangian Multipliers ◽  
The Traveling Salesman Problem ◽  
New Algorithms

CP-based Lagrangian relaxation (CP-LR) is an efficient optimization technique that combines cost-based filtering with Lagrangian relaxation in a constraint programming context. The state-of-the-art filtering algorithms for the WeightedCircuit constraint that encodes the traveling salesman problem (TSP) are based on this approach. In this paper, we propose an improved CP-LR approach that locally modifies the Lagrangian multipliers in order to increase the number of filtered values. We also introduce two new algorithms based on the latter to filter WeightedCircuit. The experimental results on TSP instances show that our algorithms allow significant gains on the resolution time and the size of the search space when compared to the state-of-the-art implementation.

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