scholarly journals Personalized Influential Community Search in Large Networks: A K-ECC-Based Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shi Meng ◽  
Hao Yang ◽  
Xijuan Liu ◽  
Zhenyue Chen ◽  
Jingwen Xuan ◽  
...  
Keyword(s):  
Social Networks ◽  
Real World ◽  
Fundamental Problem ◽  
Graph Analysis ◽  
Baseline Method ◽  
Large Networks ◽  
Community Search ◽  
Speed Up ◽  
Complex Relationships ◽  
The Given

Graphs have been widely used to model the complex relationships among entities. Community search is a fundamental problem in graph analysis. It aims to identify cohesive subgraphs or communities that contain the given query vertices. In social networks, a user is usually associated with a weight denoting its influence. Recently, some research is conducted to detect influential communities. However, there is a lack of research that can support personalized requirement. In this study, we propose a novel problem, named personalized influential k -ECC (PIKE) search, which leverages the k -ECC model to measure the cohesiveness of subgraphs and tries to find the influential community for a set of query vertices. To solve the problem, a baseline method is first proposed. To scale for large networks, a dichotomy-based algorithm is developed. To further speed up the computation and meet the online requirement, we develop an index-based algorithm. Finally, extensive experiments are conducted on 6 real-world social networks to evaluate the performance of proposed techniques. Compared with the baseline method, the index-based approach can achieve up to 7 orders of magnitude speedup.

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

2021 ◽  
Author(s):  
Mohammad Shehab ◽  
Laith Abualigah
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Second Stage ◽  
Opposition Based Learning ◽  
High Fitness ◽  
Fitness Value ◽  
Speed Up ◽  
Disruption Operator ◽  
The Given ◽  
Real World Problems

Abstract Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

scholarly journals Stacking models for nearly optimal link prediction in complex networks

2020 ◽  
Vol 117 (38) ◽  
pp. 23393-23400 ◽  
Author(s):  
Amir Ghasemian ◽  
Homa Hosseinmardi ◽  
Aram Galstyan ◽  
Edoardo M. Airoldi ◽  
Aaron Clauset
Keyword(s):  
Social Networks ◽  
Data Collection ◽  
Real World ◽  
Link Prediction ◽  
State Of The Art ◽  
Network Data ◽  
Prediction Errors ◽  
Partially Observed ◽  
Speed Up ◽  
Large Corpus

Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speed up network data collection and improve network model validation. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 550 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity using network-based metalearning to construct a series of “stacked” models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state of the art for link prediction comes from combining individual algorithms, which can achieve nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvements.

scholarly journals Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation

2018 ◽  
Author(s):  
Hao Wang ◽  
Huawei Shen ◽  
Wentao Ouyang ◽  
Xueqi Cheng
Keyword(s):  
Social Networks ◽  
Real World ◽  
Fundamental Problem ◽  
Physical Distance ◽  
Experimental Results ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Movie Recommendation ◽  
Real World Datasets ◽  
Location Based Social Networks

Point-of-interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods model the geographical influence between two POIs as the probability or propensity that the two POIs are co-visited by the same user given their physical distance. These methods assume that geographical influence between POIs is determined by their physical distance, failing to capture the asymmetry of geographical influence and the high variation of geographical influence across POIs. In this paper, we exploit POI-specific geographical influence to improve POI recommendation. We model the geographical influence between two POIs using three factors: the geo-influence of POI, the geo-susceptibility of POI, and their physical distance. Geo-influence captures POI?s capacity at exerting geographical influence to other POIs, and geo-susceptibility reflects POI?s propensity of being geographically influenced by other POIs. Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation.

scholarly journals Efficient Personalized Influential Community Search in Large Networks

2021 ◽  
Author(s):  
Yanping Wu ◽  
Jun Zhao ◽  
Renjie Sun ◽  
Chen Chen ◽  
Xiaoyang Wang
Keyword(s):  
Real World ◽  
Real Life ◽  
Core Concept ◽  
Top Down ◽  
Large Networks ◽  
Bottom Up ◽  
Community Search

AbstractCommunity search, which aims to retrieve important communities (i.e., subgraphs) for a given query vertex, has been widely studied in the literature. In the recent, plenty of research is conducted to detect influential communities, where each vertex in the network is associated with an influence value. Nevertheless, there is a paucity of work that can support personalized requirement. In this paper, we propose a new problem, i.e., maximal personalized influential community search. Given a graph G, an integer k and a query vertex u, we aim to obtain the most influential community for u by leveraging the k-core concept. To handle larger networks efficiently, two algorithms, i.e., top-down algorithm and bottom-up algorithm, are developed. In real-life applications, there may be a lot of queries issued. Therefore, an optimal index-based approach is proposed in order to meet the online requirement. In many scenarios, users may want to find multiple communities for a given query. Thus, we further extend the proposed techniques for the top-r case, i.e., retrieving r communities with the largest influence value for a given query. Finally, we conduct extensive experiments on 6 real-world networks to demonstrate the advantage of proposed techniques.

scholarly journals Community Focusing: Yet Another Query-Dependent Community Detection

2019 ◽  
Vol 33 ◽  
pp. 329-337 ◽  
Author(s):  
Zhuo Wang ◽  
Weiping Wang ◽  
Chaokun Wang ◽  
Xiaoyan Gu ◽  
Bo Li ◽  
...  
Keyword(s):  
Community Detection ◽  
Experimental Results ◽  
Connected Subgraph ◽  
Dense Subgraph ◽  
Large Networks ◽  
Community Search ◽  
Speed Up ◽  
Major Consideration ◽  
Synthetic Networks

As a major kind of query-dependent community detection, community search finds a densely connected subgraph containing a set of query nodes. As density is the major consideration of community search, most methods of community search often find a dense subgraph with many vertices far from the query nodes, which are not very related to the query nodes. Motivated by this, a new problem called community focusing (CF) is studied. It finds a community where the members are close and densely connected to the query nodes. A distance-sensitive dense subgraph structure called β-attention-core is proposed to remove the vertices loosely connected to or far from the query nodes, and a combinational density is designed to guarantee the density of a subgraph. Then CF is formalized as finding a subgraph with the largest combinational density among the β-attention-core subgraphs containing the query nodes with the largest β. Thereafter, effective methods are devised for CF. Furthermore, a speed-up strategy is developed to make the methods scalable to large networks. Extensive experimental results on real and synthetic networks demonstrate the performance of our methods.

Rewriting Minimizations for Efficient Query Answering over Ontologies

2017 ◽  
Vol 26 (05) ◽  
pp. 1760024 ◽  
Author(s):  
Tassos Venetis ◽  
Giorgos Stoilos ◽  
Vasilis Vassalos
Keyword(s):  
Real World ◽  
Experimental Evaluation ◽  
State Of The Art ◽  
Query Answering ◽  
Current Paper ◽  
Query Rewriting ◽  
Speed Up ◽  
Using Data ◽  
The Given ◽  
Data Constraints

Computing a (Union of Conjunctive Queries — UCQ) rewriting ℛ for an input query and ontology and evaluating it over the given dataset is a prominent approach to query answering over ontologies. However, ℛ can be large and complex in structure hence additional techniques, like query subsumption and data constraints, need to be employed in order to minimize ℛ and lead to an efficient evaluation. Although sound in theory, how to efficiently and effectively implement many of these techniques in practice could be challenging. For example, many systems do not implement query subsumption. In the current paper we present several practical techniques for UCQ rewriting minimization. First, we present an optimized algorithm for eliminating redundant (w.r.t. subsumption) queries as well as a novel framework for rewriting minimization using data constraints. Second, we show how these techniques can also be used to speed up the computation of ℛ in first place. Third, we integrated all our techniques in our query rewriting system IQAROS and conducted an extensive experimental evaluation using many artificial as well as challenging real-world ontologies obtaining encouraging results as, in the vast majority of cases, our system is more efficient compared to the two most popular state-of-the-art systems.

An effective community-based link prediction model for improving accuracy in social networks

2021 ◽  
pp. 1-17
Author(s):  
M. Mohamed Iqbal ◽  
K. Latha
Keyword(s):  
Social Networks ◽  
Prediction Model ◽  
Real World ◽  
Link Prediction ◽  
Centrality Measures ◽  
Community Based ◽  
Proposed Model ◽  
Similarity Indices ◽  
Community Information ◽  
The Given

Link prediction plays a predominant role in complex network analysis. It indicates to determine the probability of the presence of future links that depends on available information. The existing standard classical similarity indices-based link prediction models considered the neighbour nodes have a similar effect towards link probability. Nevertheless, the common neighbor nodes residing in different communities may vary in real-world networks. In this paper, a novel community information-based link prediction model has been proposed in which every neighboring node’s community information (community centrality) has been considered to predict the link between the given node pair. In the proposed model, the given social network graph can be divided into different communities and community centrality is calculated for every derived community based on degree, closeness, and betweenness basic graph centrality measures. Afterward, the new community centrality-based similarity indices have been introduced to compute the community centralities which are applied to nine existing basic similarity indices. The empirical analysis on 13 real-world social networks datasets manifests that the proposed model yields better prediction accuracy of 97% rather than existing models. Moreover, the proposed model is parallelized efficiently to work on large complex networks using Spark GraphX Big Data-based parallel Graph processing technique and it attains a lesser execution time of 250 seconds.

The features of transnational corporations’ advertisement in social networks

2019 ◽  
pp. 89-92
Author(s):  
Daniella Mushka ◽  
Yeva Erfan
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Wide Spectrum ◽  
Subsequent Development ◽  
Transnational Corporations ◽  
Target Audience ◽  
Social Media Marketing ◽  
Scientific Article ◽  
The Social ◽  
The Given

This scientific article considers all aspects, modern importance and growing role of the social media marketing and advertisement in the general spectrum of marketing activity for developed and developing brands. Investigational actuality and basic directions of application of all spectrum of instruments of social networks for the sake of advancement of product and the processes of forming perception of trade mark and forming the image of brand are analyzed by the authors of the article. The given scientific article highlights the most popular trends and patterns of goods and trademarks’ promotion in the world in the context of updating the concept of advertising on social networks. The bigger and more engaged your target audience is on social media networks (Instagram, Facebook, Twitter, YouTube etc), the easier it will be for you to achieve every other marketing or business goal. The importance of social media marketing’s assistance in attracting new potential clients and customers to the company is also considered in the given article. Besides that, the authors of the article list and analyse wide spectrum of basic trends considering promotion and advertising in 2019 among the well-known brands. In addition to this all, the list of the most successful publicity advertisement campaigns of this year and brands which were promoted with their assistance are listed and analysed. In the context of the study, it shows up that advertising campaigns play a significant role not only in reaching sales but also in generating overall customer loyalty to the brand. This makes it possible to argue that the most reputable brands should have an important social goal that will be positively accepted by society and target audience in addition to the high quality and usability of the products or services. Social networking is the easiest way to see the social response to your promotion and lead to an instant purchase. Therefore, relying on the experience of the already well-known multinational and transnational corporations, social media marketing should take a significant share of the overall promotion of the company. The connection between the brand and potential customer should be built on the emotions that accompany consumers when viewing ads and using products. This scientific article eventually declares conclusions and prognoses in relation to subsequent development of these instruments and platforms for advancement and branding of small and large enterprises in future. It states that emotional connection between person and brand is much more effective for the company than an expensive ad.

Systems theories

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Social Networks ◽  
Problem Solving ◽  
Natural Resources ◽  
Real World ◽  
Common Pool Resources ◽  
Ecological Systems ◽  
Effective Governance ◽  
Social Ecological Systems ◽  
Social Ecological ◽  
Real World Problem

This chapter covers systems theories relevant to understanding and working to enhance the resilience of social-ecological systems. Social-ecological systems contain natural resources, users of those resources, and the interactions between each. The theories in the chapter share lessons about how to build effective governance structures for common pool resources, how to facilitate the spread of worthwhile ideas across social networks, and how to promote collaboration for greater collective impacts than any one organization alone could achieve. Each theory is summarized succinctly and followed by guidance on how to apply it to real world problem solving.

scholarly journals A Lower Bound for the Query Phase of Contraction Hierarchies and Hub Labels and a Provably Optimal Instance-Based Schema

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 164
Author(s):  
Tobias Rupp ◽  
Stefan Funke
Keyword(s):  
Lower Bound ◽  
Lower Bounds ◽  
Real World ◽  
Road Network ◽  
Upper And Lower Bounds ◽  
Bounded Degree ◽  
Shortest Path Routing ◽  
Graph Classes ◽  
Speed Up ◽  
To Come

We prove a Ω(n) lower bound on the query time for contraction hierarchies (CH) as well as hub labels, two popular speed-up techniques for shortest path routing. Our construction is based on a graph family not too far from subgraphs that occur in real-world road networks, in particular, it is planar and has a bounded degree. Additionally, we borrow ideas from our lower bound proof to come up with instance-based lower bounds for concrete road network instances of moderate size, reaching up to 96% of an upper bound given by a constructed CH. For a variant of our instance-based schema applied to some special graph classes, we can even show matching upper and lower bounds.

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