Compressive closeness in networks

Abstract Distributed algorithms for network science applications are of great importance due to today’s large real-world networks. In such algorithms, a node is allowed only to have local interactions with its immediate neighbors; because the whole network topological structure is often unknown to each node. Recently, distributed detection of central nodes, concerning different notions of importance, within a network has received much attention. Closeness centrality is a prominent measure to evaluate the importance (influence) of nodes, based on their accessibility, in a given network. In this paper, first, we introduce a local (ego-centric) metric that correlates well with the global closeness centrality; however, it has very low computational complexity. Second, we propose a compressive sensing (CS)-based framework to accurately recover high closeness centrality nodes in the network utilizing the proposed local metric. Both ego-centric metric computation and its aggregation via CS are efficient and distributed, using only local interactions between neighboring nodes. Finally, we evaluate the performance of the proposed method through extensive experiments on various synthetic and real-world networks. The results show that the proposed local metric correlates with the global closeness centrality, better than the current local metrics. Moreover, the results demonstrate that the proposed CS-based method outperforms state-of-the-art methods with notable improvement.

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Community detection via closure extension

International Journal of Modern Physics C ◽

10.1142/s012918311850119x ◽

2018 ◽

Vol 29 (12) ◽

pp. 1850119

Author(s):

Jingming Zhang ◽

Jianjun Cheng ◽

Xiaosu Feng ◽

Xiaoyun Chen

Keyword(s):

Community Structure ◽

Computational Complexity ◽

Community Detection ◽

Network Structure ◽

Real World ◽

Prior Information ◽

State Of The Art ◽

Local Information ◽

Second Step ◽

Novel Method

Identifying community structure in networks plays an important role in understanding the network structure and analyzing the network features. Many state-of-the-art algorithms have been proposed to identify the community structure in networks. In this paper, we propose a novel method based on closure extension; it performs in two steps. The first step uses the similarity closure or correlation closure to find the initial community structure. In the second step, we merge the initial communities using Modularity [Formula: see text]. The proposed method does not need any prior information such as the number or sizes of communities, and it is able to obtain the same resulting communities in multiple runs. Moreover, it is noteworthy that our method has low computational complexity because of considering only local information of network. Some real-world and synthetic graphs are used to test the performance of the proposed method. The results demonstrate that our method can detect deterministic and informative community structure in most cases.

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Design and implementation of aggregate functions in the DLV system

Theory and Practice of Logic Programming ◽

10.1017/s1471068408003323 ◽

2008 ◽

Vol 8 (5-6) ◽

pp. 545-580 ◽

Cited By ~ 41

Author(s):

WOLFGANG FABER ◽

GERALD PFEIFER ◽

NICOLA LEONE ◽

TINA DELL'ARMI ◽

GIUSEPPE IELPA

Keyword(s):

Computational Complexity ◽

Logic Programming ◽

Real World ◽

State Of The Art ◽

Complexity Class ◽

Natural Manner ◽

Design And Implementation ◽

Finite Structures ◽

Real World Applications ◽

Aggregate Functions

AbstractDisjunctive logic programming (DLP) is a very expressive formalism. It allows for expressing every property of finite structures that is decidable in the complexity class ΣP2(=NPNP). Despite this high expressiveness, there are some simple properties, often arising in real-world applications, which cannot be encoded in a simple and natural manner. Especially properties that require the use of arithmetic operators (like sum, times, or count) on a set or multiset of elements, which satisfy some conditions, cannot be naturally expressed in classic DLP. To overcome this deficiency, we extend DLP by aggregate functions in a conservative way. In particular, we avoid the introduction of constructs with disputed semantics, by requiring aggregates to be stratified. We formally define the semantics of the extended language (called ), and illustrate how it can be profitably used for representing knowledge. Furthermore, we analyze the computational complexity of , showing that the addition of aggregates does not bring a higher cost in that respect. Finally, we provide an implementation of in DLV—a state-of-the-art DLP system—and report on experiments which confirm the usefulness of the proposed extension also for the efficiency of computation.

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Better Prediction of Mutation Score

10.36227/techrxiv.14905032 ◽

2021 ◽

Author(s):

Yossi Gil ◽

Dor Ma’ayan

Keyword(s):

Neural Networks ◽

Real World ◽

State Of The Art ◽

Future Research ◽

World Systems ◽

Reliable Measurement ◽

Mutation Score ◽

Class Level ◽

Effectiveness Prediction ◽

Better Than

<div><div><div><p>Mutation score is widely accepted to be a reliable measurement for the effectiveness of software tests. Recent studies, however, show that mutation analysis is extremely costly and hard to use in practice. We present a novel direct prediction model of mutation score using neural networks. Relying solely on static code features that do not require generation of mutants or execution of the tests, we predict mutation score with an accuracy better than a quintile. When we include statement coverage as a feature, our accuracy rises to about a decile. Using a similar approach, we also improve the state-of-the-art results for binary test effectiveness prediction and introduce an intuitive, easy-to-calculate set of features superior to previously studied sets. We also publish the largest dataset of test-class level mutation score and static code features data to date, for future research. Finally, we discuss how our approach could be integrated into real-world systems, IDEs, CI tools, and testing frameworks.</p></div></div></div>

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Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/465 ◽

2021 ◽

Author(s):

Weijia Zhang

Keyword(s):

Medical Imaging ◽

Supervised Learning ◽

Real World ◽

State Of The Art ◽

Experimental Results ◽

Weakly Supervised Learning ◽

Variational Autoencoder ◽

Label Prediction ◽

Weakly Supervised ◽

Better Than

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.

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An Input-aware Factorization Machine for Sparse Prediction

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/203 ◽

2019 ◽

Cited By ~ 2

Author(s):

Yantao Yu ◽

Zhen Wang ◽

Bo Yuan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real World ◽

State Of The Art ◽

Overall Performance ◽

Factorization Machine ◽

The Impact ◽

Novel Model ◽

Individual Input ◽

Better Than

Factorization machines (FMs) are a class of general predictors working effectively with sparse data, which represents features using factorized parameters and weights. However, the accuracy of FMs can be adversely affected by the fixed representation trained for each feature, as the same feature is usually not equally predictive and useful in different instances. In fact, the inaccurate representation of features may even introduce noise and degrade the overall performance. In this work, we improve FMs by explicitly considering the impact of individual input upon the representation of features. We propose a novel model named \textit{Input-aware Factorization Machine} (IFM), which learns a unique input-aware factor for the same feature in different instances via a neural network. Comprehensive experiments on three real-world recommendation datasets are used to demonstrate the effectiveness and mechanism of IFM. Empirical results indicate that IFM is significantly better than the standard FM model and consistently outperforms four state-of-the-art deep learning based methods.

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Better Prediction of Mutation Score

10.36227/techrxiv.14905032.v1 ◽

2021 ◽

Author(s):

Yossi Gil ◽

Dor Ma’ayan

Keyword(s):

Neural Networks ◽

Real World ◽

State Of The Art ◽

Future Research ◽

World Systems ◽

Reliable Measurement ◽

Mutation Score ◽

Class Level ◽

Effectiveness Prediction ◽

Better Than

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ROVETS

International Journal of Decision Support System Technology ◽

10.4018/ijdsst.2019070103 ◽

2019 ◽

Vol 11 (3) ◽

pp. 30-46

Author(s):

Nana Yaw Asabere ◽

Amevi Acakpovi

Keyword(s):

Real World ◽

State Of The Art ◽

Closeness Centrality ◽

Social Awareness ◽

Tie Strength ◽

Degree Centrality ◽

Breadth First Search ◽

The Social ◽

Considerable Problem ◽

Recommender Algorithm

As a result of the tremendous proliferation of sessions at academic conferences, recommending appropriate venues for researchers has become a considerable problem. In this article, the authors propose an innovative recommender algorithm called Recommendation of Venues and Environments Through Social-Awareness (ROVETS). ROVETS seeks to enhance the social awareness of attendees at a smart conference. ROVETS initially employs closeness centrality and Breadth First Search (BFS) to detect potential presenters for a target attendee. Then, the accurate tie strength between the attendees and presenters as well as the degree centrality of the presenters are computed based on similarity of their research interests. Using the computations above, ROVETS generates effective recommendations pertaining to venues for attendees who have high tie strength with presenters. Through a relevant real-world dataset, this article evaluates the proposed recommender algorithm. These experimental results validate that ROVETS exhibits favorable enhancements over other existing state-of-the-art methods.

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Dynamic Residual Dense Network for Image Denoising

Sensors ◽

10.3390/s19173809 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3809 ◽

Cited By ~ 8

Author(s):

Yuda Song ◽

Yunfang Zhu ◽

Xin Du

Keyword(s):

Real World ◽

State Of The Art ◽

Computational Cost ◽

Dynamic Network ◽

Input Image ◽

Dense Network ◽

Deep Convolutional Neural Networks ◽

Great Performance ◽

Image Noise Reduction ◽

Better Than

Deep convolutional neural networks have achieved great performance on various image restoration tasks. Specifically, the residual dense network (RDN) has achieved great results on image noise reduction by cascading multiple residual dense blocks (RDBs) to make full use of the hierarchical feature. However, the RDN only performs well in denoising on a single noise level, and the computational cost of the RDN increases significantly with the increase in the number of RDBs, and this only slightly improves the effect of denoising. To overcome this, we propose the dynamic residual dense network (DRDN), a dynamic network that can selectively skip some RDBs based on the noise amount of the input image. Moreover, the DRDN allows modifying the denoising strength to manually get the best outputs, which can make the network more effective for real-world denoising. Our proposed DRDN can perform better than the RDN and reduces the computational cost by 40 – 50 % . Furthermore, we surpass the state-of-the-art CBDNet by 1.34 dB on the real-world noise benchmark.

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Distributed Pareto Optimization for Subset Selection

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/207 ◽

2018 ◽

Cited By ~ 2

Author(s):

Chao Qian ◽

Guiying Li ◽

Chao Feng ◽

Ke Tang

Keyword(s):

Real World ◽

Large Scale ◽

State Of The Art ◽

Subset Selection ◽

Data Sets ◽

Mapreduce Framework ◽

Real World Data ◽

Real World Applications ◽

Approximation Guarantee ◽

Better Than

The subset selection problem that selects a few items from a ground set arises in many applications such as maximum coverage, influence maximization, sparse regression, etc. The recently proposed POSS algorithm is a powerful approximation solver for this problem. However, POSS requires centralized access to the full ground set, and thus is impractical for large-scale real-world applications, where the ground set is too large to be stored on one single machine. In this paper, we propose a distributed version of POSS (DPOSS) with a bounded approximation guarantee. DPOSS can be easily implemented in the MapReduce framework. Our extensive experiments using Spark, on various real-world data sets with size ranging from thousands to millions, show that DPOSS can achieve competitive performance compared with the centralized POSS, and is almost always better than the state-of-the-art distributed greedy algorithm RandGreeDi.

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Hypernetwork science via high-order hypergraph walks

EPJ Data Science ◽

10.1140/epjds/s13688-020-00231-0 ◽

2020 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Sinan G. Aksoy ◽

Cliff Joslyn ◽

Carlos Ortiz Marrero ◽

Brenda Praggastis ◽

Emilie Purvine

Keyword(s):

Real World ◽

Network Science ◽

Generative Models ◽

Closeness Centrality ◽

High Order ◽

Structured Data ◽

Graph Distance ◽

Connected Component ◽

Generalize Graph ◽

Clustering Coefficients

Abstract We propose high-order hypergraph walks as a framework to generalize graph-based network science techniques to hypergraphs. Edge incidence in hypergraphs is quantitative, yielding hypergraph walks with both length and width. Graph methods which then generalize to hypergraphs include connected component analyses, graph distance-based metrics such as closeness centrality, and motif-based measures such as clustering coefficients. We apply high-order analogs of these methods to real world hypernetworks, and show they reveal nuanced and interpretable structure that cannot be detected by graph-based methods. Lastly, we apply three generative models to the data and find that basic hypergraph properties, such as density and degree distributions, do not necessarily control these new structural measurements. Our work demonstrates how analyses of hypergraph-structured data are richer when utilizing tools tailored to capture hypergraph-native phenomena, and suggests one possible avenue towards that end.

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