Random Walk by User Trust and Temporal Issues toward Sparsity Problem in Soci...Sc Huang
Random Walk by User Trust and Temporal Issues toward Sparsity Problem in Social Tagging Recommender Systems
The document discusses using random walks and temporal factors to address sparsity problems in social tagging recommender systems. It introduces related work on item-based collaborative filtering, random walk recommendations, and models that learn influence probabilities. It then describes using random walks starting from users or items, and incorporating trust networks and influence powers to provide recommendations. Finally, it discusses addressing cold start problems, temporal decay issues, and experiment design.
I gave a talk at Xerox Europe Research Center (Grenoble, France) on Mar. 3. 2014. This included a couple of projects in my research lab that examine shared rationales in group activities.
13 An Introduction to Stochastic Actor-Oriented Models (aka SIENA)dnac
This document provides an introduction to Stochastic Actor-Oriented Models (SAOMs), also known as SIENA models. It discusses when SAOMs are appropriate to use, provides an overview of the general SAOM form, and covers key components like the network and behavior objective functions and rate functions. The presentation also outlines how SAOMs are estimated and fitted to data, provides an empirical example, and discusses extensions. SAOMs model how networks and behaviors change over time as actors make micro-level decisions to maximize their objective functions.
This document describes the design and implementation of a Bayesian network to predict reputation in virtual learning communities. It discusses motivations for the work, provides definitions of key concepts like trust and reputation, outlines the design of the Bayesian network including factors like direct experience and reputation. It then details the implementation of a prototype in Moodle that allows users to provide positive or negative feedback on resources and activities. The prototype calculates reputation scores using the Bayesian network and aggregates user interactions. Finally, it evaluates the prototype using a real virtual learning community and compares the results to the original Bayesian network model.
[Decisions2013@RecSys]The Role of Emotions in Context-aware RecommendationYONG ZHENG
The document discusses the role of emotions in context-aware recommender systems (CARS). It explores two classes of CARS algorithms: context-aware splitting approaches and differential context modeling. For context-aware splitting approaches, it examines which emotional contexts are most frequently used to split items or users. For differential context modeling, it analyzes which emotional dimensions are selected or weighted most highly for different algorithm components. The experimental results found that the emotions of end emotion and dominant emotion were the most influential across approaches. User splitting also generally outperformed item splitting.
This document discusses how to effectively communicate program impacts to intended users. It emphasizes the importance of:
1) Clarifying the specific impacts, or "of what" and "on what", through a well-defined program theory of change.
2) Understanding the intended users, or "for whom", by identifying their decision needs and how findings will be applied.
3) Designing impact evaluations and reports that meet the specific information needs of intended users so findings can be believed and applied to inform decisions.
This document outlines a movie recommendation system project built using collaborative filtering. The project aims to build a recommendation engine that suggests movies to users based on their preferences and watching history. It will use the MovieLens dataset and implement item-based collaborative filtering. The key steps include importing libraries, preprocessing the data, building the recommendation model using collaborative filtering, and evaluating the model's performance. Collaborative filtering works by comparing a user's preferences to other users to find patterns and provide personalized recommendations. The document also discusses some disadvantages of collaborative filtering like the cold-start problem and difficulty including additional metadata.
DYNAMIC LARGE SCALE DATA ON TWITTER USING SENTIMENT ANALYSIS AND TOPIC MODELINGAndry Alamsyah
1. The document presents a case study analyzing tweets about Uber using sentiment analysis and topic modeling to understand public opinion from large-scale social media data.
2. Sentiment analysis classified tweets as positive, negative, or neutral, while topic modeling identified dominant topics of discussion, like promotions or driver complaints.
3. The analyses found that positive tweets often discussed promotions while negative tweets addressed issues like sexual harassment allegations or unsatisfactory drivers.
Random Walk by User Trust and Temporal Issues toward Sparsity Problem in Soci...Sc Huang
Random Walk by User Trust and Temporal Issues toward Sparsity Problem in Social Tagging Recommender Systems
The document discusses using random walks and temporal factors to address sparsity problems in social tagging recommender systems. It introduces related work on item-based collaborative filtering, random walk recommendations, and models that learn influence probabilities. It then describes using random walks starting from users or items, and incorporating trust networks and influence powers to provide recommendations. Finally, it discusses addressing cold start problems, temporal decay issues, and experiment design.
I gave a talk at Xerox Europe Research Center (Grenoble, France) on Mar. 3. 2014. This included a couple of projects in my research lab that examine shared rationales in group activities.
13 An Introduction to Stochastic Actor-Oriented Models (aka SIENA)dnac
This document provides an introduction to Stochastic Actor-Oriented Models (SAOMs), also known as SIENA models. It discusses when SAOMs are appropriate to use, provides an overview of the general SAOM form, and covers key components like the network and behavior objective functions and rate functions. The presentation also outlines how SAOMs are estimated and fitted to data, provides an empirical example, and discusses extensions. SAOMs model how networks and behaviors change over time as actors make micro-level decisions to maximize their objective functions.
This document describes the design and implementation of a Bayesian network to predict reputation in virtual learning communities. It discusses motivations for the work, provides definitions of key concepts like trust and reputation, outlines the design of the Bayesian network including factors like direct experience and reputation. It then details the implementation of a prototype in Moodle that allows users to provide positive or negative feedback on resources and activities. The prototype calculates reputation scores using the Bayesian network and aggregates user interactions. Finally, it evaluates the prototype using a real virtual learning community and compares the results to the original Bayesian network model.
[Decisions2013@RecSys]The Role of Emotions in Context-aware RecommendationYONG ZHENG
The document discusses the role of emotions in context-aware recommender systems (CARS). It explores two classes of CARS algorithms: context-aware splitting approaches and differential context modeling. For context-aware splitting approaches, it examines which emotional contexts are most frequently used to split items or users. For differential context modeling, it analyzes which emotional dimensions are selected or weighted most highly for different algorithm components. The experimental results found that the emotions of end emotion and dominant emotion were the most influential across approaches. User splitting also generally outperformed item splitting.
This document discusses how to effectively communicate program impacts to intended users. It emphasizes the importance of:
1) Clarifying the specific impacts, or "of what" and "on what", through a well-defined program theory of change.
2) Understanding the intended users, or "for whom", by identifying their decision needs and how findings will be applied.
3) Designing impact evaluations and reports that meet the specific information needs of intended users so findings can be believed and applied to inform decisions.
This document outlines a movie recommendation system project built using collaborative filtering. The project aims to build a recommendation engine that suggests movies to users based on their preferences and watching history. It will use the MovieLens dataset and implement item-based collaborative filtering. The key steps include importing libraries, preprocessing the data, building the recommendation model using collaborative filtering, and evaluating the model's performance. Collaborative filtering works by comparing a user's preferences to other users to find patterns and provide personalized recommendations. The document also discusses some disadvantages of collaborative filtering like the cold-start problem and difficulty including additional metadata.
DYNAMIC LARGE SCALE DATA ON TWITTER USING SENTIMENT ANALYSIS AND TOPIC MODELINGAndry Alamsyah
1. The document presents a case study analyzing tweets about Uber using sentiment analysis and topic modeling to understand public opinion from large-scale social media data.
2. Sentiment analysis classified tweets as positive, negative, or neutral, while topic modeling identified dominant topics of discussion, like promotions or driver complaints.
3. The analyses found that positive tweets often discussed promotions while negative tweets addressed issues like sexual harassment allegations or unsatisfactory drivers.
Finding Pattern in Dynamic Network AnalysisAndry Alamsyah
1) The document analyzes social network properties like nodes, edges, average degree, diameter and average path length for different companies on Twitter over time.
2) It finds that network properties generally indicate more user interactions and information sharing on weekdays compared to weekends.
3) However, the diameter and average path length are often lowest on weekends, suggesting information spreads more quickly at those times due to the network structure.
This document summarizes a study that analyzed user sentiment and social influence in an online travel forum. The researchers constructed a user network based on forum interactions and used sentiment analysis to determine each user's sentiment score. They then applied a social influence model called a Linear Network Autocorrelation Model (LNAM) to test whether a user's sentiment is influenced by the sentiments of their peers in the network. The LNAM results showed that user sentiments are contagious, with a statistically significant influence parameter. Therefore, the researchers concluded that a user's happiness is influenced by the happiness of their peers in the network.
Stochastic actor-oriented models (SAOMs) summarize the key components and estimation process of these models. SAOMs model how networks and behaviors change over time as a result of endogenous network effects and influence between connected individuals. The models estimate parameters representing these effects to predict tie formation and changes in behaviors. SAOMs account for selection into the network based on attributes as well as social influence processes within the network. Estimation involves maximum likelihood to estimate parameters of network and behavior functions that represent how individuals make network and behavioral decisions.
Hybrid sentiment and network analysis of social opinion polarization icoictAndry Alamsyah
The rapid growth of social media and user generated contents (UGC) has provided a rich source of potentially relevant data. The problems arise on how to summarize those data to understand and transforming it into information. Twitter as one of the most popular social networking and micro-blogging service can be analyzed in terms of content produced with sentiment analysis. On the other hand, some types of networks can also be constructed to analyze the social network structure and network properties. This research intended to combine those content and structural approaches into hybrid approach for identifies social opinion polarization, this is in the form of conversation network. Sentiment analysis used to determine public sentiment, and social network analysis used to analyze the structure of the network, detecting communities and influential actors in the network. Using this hybrid approach, we have comprehensive understanding about social opinion polarization. As case study, we present real social opinion polarization about reclamation issue in Indonesia.
This document provides an overview of stochastic actor-oriented models (SAOMs), including:
1. The general components of SAOMs including network and behavior objective functions that determine how and when actors change their ties and behaviors.
2. The estimation procedure which uses simulations to refine parameter estimates and minimize the deviation between simulated and observed network statistics.
3. Examples of how to interpret the output including checking for convergence of the model to the observed data.
Big-O(Q) VLDB 2015 Keynote: Social Network Analytics: Beyond the ObviousLaks Lakshmanan
This document summarizes research on social network analytics beyond basic influence maximization. It discusses tracking how events and stories evolve online, facilitating organization of local social events, and models that go beyond assuming influenced users adopt to capture distinctions between influence and adoption. It also covers alternative optimization problems like minimizing seed budget or propagation time. Models are discussed that consider factors like customer valuations and maximize profit rather than just influence spread.
This document summarizes research on social network analytics beyond basic influence maximization. It discusses tracking how events and stories evolve online, facilitating organization of local social events, and models that go beyond assuming influenced users adopt to capture distinctions between influence and adoption. It also covers alternative optimization problems like minimizing seed budget or propagation time. Models are discussed that consider factors like customer valuations and maximize profit rather than just influence spread.
This document discusses social media analytics and some of the challenges involved. It provides an overview of different types of social media analytics including sentiment analysis, social network analysis, and image/video analysis. Real-time and non-real-time customer and competitive analytics are also discussed. The document outlines some of the processes involved in social media analytics and highlights challenges like bias in social media data and unstructured social media data.
Raising Awareness and Learning Practices of Citizens for Energy SavingsAndreas Kamilaris
Raising awareness about energy savings through social influence and feedback. The document discusses how awareness can be raised by focusing on the local level, using a project-based learning framework. Effective strategies include frequent feedback on energy usage, comparisons to historical usage and others', as well as social pressures like competitions and rankings. An online social energy project at NUS found that students responded best to comparisons, goal setting, and feedback on savings. Future work could personalize feedback strategies and better understand motivations for different groups.
WSDM 2018 Tutorial on Influence Maximization in Online Social NetworksCigdem Aslay
In this tutorial, we extensively survey the research on social influence propagation and maximization, with a focus on the recent algorithmic and theoretical advances. To this end, we provide detailed reviews of the latest research effort devoted to (i) improving the efficiency and scalability of the influence maximization algorithms; (ii) context-aware modeling of the influence maximization problem to better capture real-world marketing scenarios; (iii) modeling and learning of real-world social influence; (iv) bridging the gap between social advertising and viral marketing.
Brief tutorial on Influence and Homophily in social networks. Key concepts. How to distinguish influence from correlation. Information diffusion processes. Influence Maximization Problem
and viral marketing.
Inspiring impact - let your impact do the askingwalescva
The document discusses the importance of impact measurement for organizations. It introduces the Code of Good Impact Practice and an online self-assessment toolkit called Measuring Up! that is based on the code's 8 principles. The document also covers defining impact, choosing impact measurement tools, and communicating impact results to both internal and external stakeholders. Funders want evidence of impact, so impact practice is important for organizations to demonstrate the difference they make and ensure their work is effective.
While interning at GSD&M, a client, LL Bean, asked for recommendations on influencer marketing services. They were looking into TRAACKR, and wanted a comparison.
This document discusses a methodology for distinguishing between social influence and homophily effects in network data. It proposes using randomization tests that generate permuted data sets under different null hypotheses (no homophily, no influence). The approach calculates correlation gains between attribute and link changes and compares them to the distribution from permuted data. It was shown to work on synthetic and real social network data, identifying varying degrees of influence and homophily between groups. The methodology provides a robust way to test for these effects without distributional assumptions.
Contribution to proactivity in mobile context-aware recommender systemsDaniel Gallego Vico
1) The document proposes methods for incorporating proactivity into mobile context-aware recommender systems (CARS) and evaluates their impact on user experience.
2) An architecture is presented for building social mobile CARS that integrates various social data sources while addressing privacy, cross-platform use, and cold start issues.
3) A model is described for generating proactive recommendations in mobile CARS based on assessing the appropriateness of the user's situation and suitability of item recommendations.
This document discusses downward accountability in development organizations and the role of power in empowering beneficiaries. It summarizes a study of two NGOs in India - Rural Life and Unison - and their approaches to governance, communication, planning, implementation, monitoring and evaluation. While Rural Life took a top-down hierarchical approach, Unison engaged communities collaboratively. As a result, Unison was more effective at empowering communities, reducing dependence and challenging social roles, while Rural Life perpetuated dependency. For downward accountability to achieve empowerment, the study concludes development organizations must critically address underlying power imbalances and reasons for beneficiaries' disempowerment.
Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....R R
Rezapour R, Diesner J (2017) Classification and Detection of Micro-Level Impact of Issue-Focused Films based on Reviews. Proceedings of 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017), Portland, OR.
This document discusses evaluation methodology for practices in science communication. It begins by noting the lack of systematic evaluation has made it difficult to compare practices, develop theories, and ensure accountability. The author argues for developing a common evaluation language while acknowledging the diversity of science communication. A key challenge is that practices have diverse purposes and actors. The author proposes using program theory and logic models to systematically evaluate practices in an ex post facto manner. This involves practitioners describing the purposes and means of a practice after completion to facilitate evaluation. The discussion considers how to account for change and complexity in program theories. The goal of developing evaluation is to improve practices for public benefit rather than administrative control.
Finding Pattern in Dynamic Network AnalysisAndry Alamsyah
1) The document analyzes social network properties like nodes, edges, average degree, diameter and average path length for different companies on Twitter over time.
2) It finds that network properties generally indicate more user interactions and information sharing on weekdays compared to weekends.
3) However, the diameter and average path length are often lowest on weekends, suggesting information spreads more quickly at those times due to the network structure.
This document summarizes a study that analyzed user sentiment and social influence in an online travel forum. The researchers constructed a user network based on forum interactions and used sentiment analysis to determine each user's sentiment score. They then applied a social influence model called a Linear Network Autocorrelation Model (LNAM) to test whether a user's sentiment is influenced by the sentiments of their peers in the network. The LNAM results showed that user sentiments are contagious, with a statistically significant influence parameter. Therefore, the researchers concluded that a user's happiness is influenced by the happiness of their peers in the network.
Stochastic actor-oriented models (SAOMs) summarize the key components and estimation process of these models. SAOMs model how networks and behaviors change over time as a result of endogenous network effects and influence between connected individuals. The models estimate parameters representing these effects to predict tie formation and changes in behaviors. SAOMs account for selection into the network based on attributes as well as social influence processes within the network. Estimation involves maximum likelihood to estimate parameters of network and behavior functions that represent how individuals make network and behavioral decisions.
Hybrid sentiment and network analysis of social opinion polarization icoictAndry Alamsyah
The rapid growth of social media and user generated contents (UGC) has provided a rich source of potentially relevant data. The problems arise on how to summarize those data to understand and transforming it into information. Twitter as one of the most popular social networking and micro-blogging service can be analyzed in terms of content produced with sentiment analysis. On the other hand, some types of networks can also be constructed to analyze the social network structure and network properties. This research intended to combine those content and structural approaches into hybrid approach for identifies social opinion polarization, this is in the form of conversation network. Sentiment analysis used to determine public sentiment, and social network analysis used to analyze the structure of the network, detecting communities and influential actors in the network. Using this hybrid approach, we have comprehensive understanding about social opinion polarization. As case study, we present real social opinion polarization about reclamation issue in Indonesia.
This document provides an overview of stochastic actor-oriented models (SAOMs), including:
1. The general components of SAOMs including network and behavior objective functions that determine how and when actors change their ties and behaviors.
2. The estimation procedure which uses simulations to refine parameter estimates and minimize the deviation between simulated and observed network statistics.
3. Examples of how to interpret the output including checking for convergence of the model to the observed data.
Big-O(Q) VLDB 2015 Keynote: Social Network Analytics: Beyond the ObviousLaks Lakshmanan
This document summarizes research on social network analytics beyond basic influence maximization. It discusses tracking how events and stories evolve online, facilitating organization of local social events, and models that go beyond assuming influenced users adopt to capture distinctions between influence and adoption. It also covers alternative optimization problems like minimizing seed budget or propagation time. Models are discussed that consider factors like customer valuations and maximize profit rather than just influence spread.
This document summarizes research on social network analytics beyond basic influence maximization. It discusses tracking how events and stories evolve online, facilitating organization of local social events, and models that go beyond assuming influenced users adopt to capture distinctions between influence and adoption. It also covers alternative optimization problems like minimizing seed budget or propagation time. Models are discussed that consider factors like customer valuations and maximize profit rather than just influence spread.
This document discusses social media analytics and some of the challenges involved. It provides an overview of different types of social media analytics including sentiment analysis, social network analysis, and image/video analysis. Real-time and non-real-time customer and competitive analytics are also discussed. The document outlines some of the processes involved in social media analytics and highlights challenges like bias in social media data and unstructured social media data.
Raising Awareness and Learning Practices of Citizens for Energy SavingsAndreas Kamilaris
Raising awareness about energy savings through social influence and feedback. The document discusses how awareness can be raised by focusing on the local level, using a project-based learning framework. Effective strategies include frequent feedback on energy usage, comparisons to historical usage and others', as well as social pressures like competitions and rankings. An online social energy project at NUS found that students responded best to comparisons, goal setting, and feedback on savings. Future work could personalize feedback strategies and better understand motivations for different groups.
WSDM 2018 Tutorial on Influence Maximization in Online Social NetworksCigdem Aslay
In this tutorial, we extensively survey the research on social influence propagation and maximization, with a focus on the recent algorithmic and theoretical advances. To this end, we provide detailed reviews of the latest research effort devoted to (i) improving the efficiency and scalability of the influence maximization algorithms; (ii) context-aware modeling of the influence maximization problem to better capture real-world marketing scenarios; (iii) modeling and learning of real-world social influence; (iv) bridging the gap between social advertising and viral marketing.
Brief tutorial on Influence and Homophily in social networks. Key concepts. How to distinguish influence from correlation. Information diffusion processes. Influence Maximization Problem
and viral marketing.
Inspiring impact - let your impact do the askingwalescva
The document discusses the importance of impact measurement for organizations. It introduces the Code of Good Impact Practice and an online self-assessment toolkit called Measuring Up! that is based on the code's 8 principles. The document also covers defining impact, choosing impact measurement tools, and communicating impact results to both internal and external stakeholders. Funders want evidence of impact, so impact practice is important for organizations to demonstrate the difference they make and ensure their work is effective.
While interning at GSD&M, a client, LL Bean, asked for recommendations on influencer marketing services. They were looking into TRAACKR, and wanted a comparison.
This document discusses a methodology for distinguishing between social influence and homophily effects in network data. It proposes using randomization tests that generate permuted data sets under different null hypotheses (no homophily, no influence). The approach calculates correlation gains between attribute and link changes and compares them to the distribution from permuted data. It was shown to work on synthetic and real social network data, identifying varying degrees of influence and homophily between groups. The methodology provides a robust way to test for these effects without distributional assumptions.
Contribution to proactivity in mobile context-aware recommender systemsDaniel Gallego Vico
1) The document proposes methods for incorporating proactivity into mobile context-aware recommender systems (CARS) and evaluates their impact on user experience.
2) An architecture is presented for building social mobile CARS that integrates various social data sources while addressing privacy, cross-platform use, and cold start issues.
3) A model is described for generating proactive recommendations in mobile CARS based on assessing the appropriateness of the user's situation and suitability of item recommendations.
This document discusses downward accountability in development organizations and the role of power in empowering beneficiaries. It summarizes a study of two NGOs in India - Rural Life and Unison - and their approaches to governance, communication, planning, implementation, monitoring and evaluation. While Rural Life took a top-down hierarchical approach, Unison engaged communities collaboratively. As a result, Unison was more effective at empowering communities, reducing dependence and challenging social roles, while Rural Life perpetuated dependency. For downward accountability to achieve empowerment, the study concludes development organizations must critically address underlying power imbalances and reasons for beneficiaries' disempowerment.
Classification and Detection of Micro-Level Impact-CSCW2017 (Link: http://dl....R R
Rezapour R, Diesner J (2017) Classification and Detection of Micro-Level Impact of Issue-Focused Films based on Reviews. Proceedings of 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017), Portland, OR.
This document discusses evaluation methodology for practices in science communication. It begins by noting the lack of systematic evaluation has made it difficult to compare practices, develop theories, and ensure accountability. The author argues for developing a common evaluation language while acknowledging the diversity of science communication. A key challenge is that practices have diverse purposes and actors. The author proposes using program theory and logic models to systematically evaluate practices in an ex post facto manner. This involves practitioners describing the purposes and means of a practice after completion to facilitate evaluation. The discussion considers how to account for change and complexity in program theories. The goal of developing evaluation is to improve practices for public benefit rather than administrative control.
Agents vs Users: Visual Recommendation of Research Talks with Multiple Dimens...Katrien Verbert
Published in ACM TiiS: Verbert, K., Parra, D., & Brusilovsky, P. (2016). Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance. ACM Transactions on Interactive Intelligent Systems (TiiS), 6(2), 11.
Presented at IUI 2017
This document discusses using social network analysis to design and evaluate family planning programs. It begins by defining social network analysis and explaining why taking a social network approach is important when designing health programs. It then discusses different ways social networks can support the diffusion of family planning innovations through social learning and social influence. The document provides examples of how to incorporate social network analysis into program design, monitoring, and evaluation and discusses six common social network intervention approaches. It emphasizes the importance of understanding social networks and relationships within a community in order to design effective programs.
- The past is not always a guide to the future, and evaluation methods and approaches must evolve over time to remain relevant (path dependence).
- While experimental methods have advantages in establishing causality, society is complex and experiments have limitations; mixed qualitative and quantitative methods are needed.
- Evaluation should put values like equity, inclusion and sustainability at the center; examine power imbalances and rules that concentrate wealth; and use new metrics of well-being beyond just GDP.
- Given changes in development landscapes, evaluation must also consider the broader architecture of aid and factors beyond just aid flows.
Similar to KICSS2020 Invited Talk 2: Prof. Quan Bai from University of Tasmania (20)
Jawad Haqbeen from Nagoya Institute of Technology Jawad Haqbeen
This document discusses using a conversational agent to facilitate discussion and generate ideas to help achieve the UN Sustainable Development Goals (SDGs). It conducted an experiment using an agent to moderate online discussions on SDG topics. The results showed that discussions with agent facilitation generated more ideas and supporting arguments compared to discussions without an agent. The agent helped engage more participants, extract valuable insights, and lead to more deliberation. Future work involves larger scale discussions to further harness the collective intelligence of crowds to help policymaking efforts towards achieving the SDGs.
TS2-1: Shoko Kimura from Japan Advanced Institute of Science and TechnologyJawad Haqbeen
Session Chair: Kiyota Hashimoto
Session Theme: Online Discussion and Cooperation
Session Number: 2
Paper No: 11
Session and Talk No: TS2-1
Type: Short
Co-authors: Shoko Kimura, Susumu Kunifuji and Takayuki Ito
Title: A Comparative Study of the Effects of Clapping Hands Sounds and Gesture Presentation During Online Meetings
TS2-2: Shun Shiramatsu from Nagoya Institute of TechnologyJawad Haqbeen
Session Chair: Kiyota Hashimoto
Session Theme: Online Discussion and Cooperation
Session Number: 2
Paper No: 28
Session and Talk No: TS2-2
Type: Short
Co-authors: Shun Shiramatsu and Yasunobu Igarashi
Title: A Preliminary Consideration toward Evidence-based Consensus Building through Human-Agent Collaboration on Semantic Authoring Platform
TS2-5: Jie Jiang from Japan Advanced Institute of Science and TechnologyJawad Haqbeen
Session Chair: Kyota Hashimoto
Session Theme: Online Discussion and Cooperation
Session Number: 2
Paper No: 6
Session and Talk No: TS2-5
Type: Full
Co-authors: Jie Jiang, Nagai Yukari, Yuizono Takaya and Yang Yu
Title: Research on New Quantitative Methods to Understand the Vitality of Urban Public Space
TS2-4: Sofia Sahab from Nagoya Institute of TechnologyJawad Haqbeen
Session Chair: Kiyota Hashimoto
Session Theme: Online Discussion and Cooperation
Session Number: 2
Paper No: 3
Session and Talk No: TS2-4
Type: Full
Co-authors: Sofia Sahab, Takayuki Ito, Jawad Haqbeen and Shun Okuhara
Title: Towards an Insights-Driven Participatory Tool for Social Sustainability in the context of Neighborhood Functions of Gozars in Kabul City
The 15th International Conference on Knowledge, Information and Creativity Support System Program had two days of sessions and talks. Day 1 included 3 sessions, 2 invited talks, an award ceremony and social events. Day 2 featured 2 sessions, 2 invited talks and a closing ceremony.
TS1-2: Xiuxia Cui from Japan Advanced Institute of Science and Technology Jawad Haqbeen
Session Chair: Thanaruk Theeramunkong
Session Theme: Creative Research Environments & their Performance
Session Number: 1
Paper No: 18
Session and Talk No: TS1-2
Type: Full
Co-authors: Xiuxia Cui, Yukari Nagai and Xiaoxiao Liu
Title: A Study on the Elderly House with Supportive Service from the Viewpoint of Creativity
The 15th International Conference on Knowledge, Information and Creativity Support System (KICSS2020) will be held online on November 25-26, 2020 and hosted by D-Agree. The conference aims to facilitate technology and knowledge exchange between international researchers in fields related to knowledge science, information systems, creativity support systems, and complex systems modeling. It will cover a broad range of topics related to knowledge engineering, information technology, creativity support systems, and complex systems modeling.
Invited Talk 1: Dr. Sanparith MarukatatJawad Haqbeen
Sanparith Marukatat will give an invited talk titled "AI in medicine" from 1:00-1:50 PM on November 25, 2020. Dr. Marukatat received his computer science degree in 1998 from University of Franche-Comté, Besançon in France and completed his doctoral thesis at University Paris 6 in 2004. He currently works in the Image Processing and Understanding team at the National Electronics and Computer Technology Center, where his research interests include machine learning, statistical pattern recognition, and AI.
Professor Kwei-Jay Lin will give an invited talk titled "The AutoCoach Smart Agent Project: AI with a Personality". He will discuss the AutoCoach project, which uses an Android application and sensors to monitor a driver's behavior in real time and compare it to their history and others. Based on this analysis, AutoCoach offers personalized recommendations to make drivers aware of potential problems and gradually improve their behavior. The talk will cover AutoCoach's system architecture, user interface design, and early experiences using AI technologies to collect driving data, analyze behaviors, match users to driving styles, and provide intelligent feedback to persuade safer driving.
TS5-3: Shohei Watanabe from Akita Prefectural UniversityJawad Haqbeen
Session Chair: Shun Okuhara
Session Theme: Education and Support
Session Number: 5
Paper No: 25
Session and Talk No: TS5-3
Type: Short
Co-authors: Shohei Watanabe and Ryo Sugawara
Title: Consideration of group approaches based on Japanese group principle
TS5-4: Ming Yi from Japan Advanced Institute of Science and TechnologyJawad Haqbeen
Please listen to the presentation, read detailed slides and return to first post to make your comments below the corresponding paper author's post.
Session Chair: Shun Okuhara
Session Theme: Education and Support
Session Number: 5
Paper No: 5
Session and Talk No: TS5-4
Type: Short
Co-authors: Ming Yi, Kecheng Lai and Yukari Nagai
Title: Using Symbol Designs for a Cooking Puzzle Game as Training Material for Error-less Learning for MCI
TS5-5: Gao Wei from Japan Advanced Institute of Science and TechnologyJawad Haqbeen
Please listen to the presentation, read detailed slides and return to first post to make your comments below the corresponding paper author's post.
Session Chair: Shun Okuhara
Session Theme: Education and Support
Session Number: 5
Paper No: 9
Session and Talk No: TS5-5
Type: Short
Co-authors: Gao Wei, Yukari Nagai and Zhang Ruifeng
Title: Influencing Factors and Intervention Strategies of Kindergarten Outdoor Environment on Children's Accidental Injury
TS5-7: Tessai Hayama from Nagaoka University of TechnologyJawad Haqbeen
Please listen to the presentation, read detailed slides and return to first post to make your comments below the corresponding paper author's post.
Session Chair: Shun Okuhara
Session Theme: Education and Support
Session Number: 5
Paper No: 8
Session and Talk No: TS5-7
Type: Full
Co-authors: Tessai Hayama and Shuma Sato
Title: Supporting Concept-Map Creation in Video-Based Learning based on Concept-Map Components Provision
TS5-1: Takashi Sakuma from Chiba Prefectural University of Health SciencesJawad Haqbeen
This document discusses using e-books to revitalize tourism at destinations. It proposes creating e-book guides for specific locations that can be easily updated with timely information and distributed digitally. The plan involves surveying existing e-books, considering how to leverage e-books' strengths for tourism, setting up an e-book creation environment, defining requirements, creating sample e-books, experimenting, and verifying effectiveness through interviews. The document provides details on using the Sigil e-book editor to easily create e-books in EPUB format for distribution. The goal is to engage individuals and local businesses in producing e-book guides to promote tourism.
TS3-3: Naoki Kawamura from Nagoya Institute of TechnologyJawad Haqbeen
1) The document proposes extracting a more specific discussion structure called "Oppose and IBIS structure" from online discussion forums to allow automated facilitation agents to perform more diverse facilitations.
2) This structure adds an "Oppose structure" element to the existing IBIS structure, and can increase the number of potential facilitations from 80 to 107.
3) The authors define a method to extract nodes using a tree structure classifier and restrict link extraction to sentences within 20 distances, achieving better performance than existing methods in experiments on real discussion data.
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The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
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Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
2. • About me:
• Associate Professor
• Leader of UTAS AI Research Group
• 2011-2019: Auckland University of Technology, New Zealand
• 2009-2011: CSIRO, Australia
• My research:
• Multi-agent systems
• Data mining
• Agent-based modelling
• Distributed systems:
• Blockchain
• AI applications
• CV-based sea-floor monitoring
• Deep learning based healthcare
2
4. University of Tasmania
• Founded in 1890
• 4th oldest university in Australia
• The university was ranked in the top 10 research universities in
Australia and in the top two per cent of universities worldwide in
the Academic Ranking of World Universities.
4
Salamanca Market, Hobart
University of Tasmania, Sandy Bay
6. Outline
6
• Background and Preliminaries
• Influence propagation modelling for complex systems
• Agent-based Influence Diffusion Model
• Multiple influence diffusion modelling
• Influence-based proactive recommendation
• Summary
7. 7
• Social Influence
• A force that an individual (i.e., the influencer) exerts on other individuals to
introduce a change of the behaviour and/or opinion
• Emotions, options or behaviours are affected by others
• Example: my friends are using IPhone, I will buy one soon
8. • Word of Mouth Marketing (WoMM)
• 'seeding' a message in a network, rewarding regular
consumers to engage
• Viral marketing
• use pre-existing social networking services and
other technologies to try to produce increases
in brand awareness or to achieve other marketing
objectives (such as product sales)
8
9. • Influence propagation comes with cost and risks
• Cannot infinitely include influencers (e.g., bloggers)
• Influence can be negative
• Influence maximisation: find a seed-set of influential nodes such that
by targeting them we maximize the spread of viral propagation
• Maximize influence with limited budgets
• To find a solution is NP-hard
• Seed set and seed selection
9
11. • The IC model
• Every arc (vi, vj) has associated the probability pij of vi influencing vj
• Influence processes in discrete steps
• In each step, an influencer has a chance to influence its
neighbours with the probability on the arcs.
11
vi
vj
pij
12. • Computational overhead
• Traditional seed selection algorithms based on the
centralized diffusion models cause computational
overhead with the expansion of social network.
• Rely on global view
• State-of-the-art influence diffusion models, such as IC and
LT, assume that topological structure is available
• Not practical when the network topology is unavailable.
12
Limitations of classic models
13. • Intelligent agent and multi-agent systems (MAS):
• Autonomous computational entity
• Can achieve reasoning and decision making based on local knowledge
• Can interact with other agents
Agent-based modelling
13
MAS is a perfect tool for modelling complex systems
14. • Micro-level modelling rather than macro-level
• Users are modelled as autonomous and self-directed agents.
• Agent states: Positively Activated (PA), Negatively Activated (NA), Inactive (IA)
• Influence diffusion demonstrates a decentralized evolutionary pattern driven by
the individual’s actions
Agent-based Influence Diffusion Model (AIDM)
14
Possibility to be influenced by
the neighbours
Personal preference to stick on
the item
15. • Agent
• An agent is defined as a vertex 𝑣𝑖 in a directed weighted social network 𝐺 = 𝑉, 𝐸 , the
weight (strength) of edge 𝑒𝑖𝑗 denotes the influence propagation probability from 𝑣𝑖 to 𝑣𝑗.
Agent 𝑣𝑗 has a preference state toward a particular item 𝑖 𝑥, which can be represented as
𝑠𝑗𝑥, 𝑠𝑗𝑥 ∈ {𝑃𝐴, 𝑁𝐴, 𝐼𝐴}. The preference of a specific agent can be derived from the ratings to
items {𝑟𝑗𝑥|𝑣𝑗 ∈ 𝑉, 𝑖 𝑥 ∈ 𝐼}.
• Social pressure
• Social pressure 𝑠𝑝𝑗𝑥|𝑆 is defined as the influence agent 𝑣𝑗 received from its immediate
neighbours Г 𝑣j , to change or stick on its opinion towards item 𝑖 𝑥 to one particular
preference state 𝑆, 𝑆 ∈ {𝑃𝐴, 𝑁𝐴, 𝐼𝐴}.
• The value of social pressure is usually measured by examining the numbers of immediate
neighbours with different preference states.
Formal definitions
𝑖𝑝𝑝𝑖𝑗 = 𝑐𝑝𝑠𝑖𝑗 ∙
|𝐼𝑖|
|𝐼𝑖 ∪ 𝐼𝑗|
15
16. • Prior Commitment Level (PCL)
• PCL is formally defined as agent 𝑣𝑖 estimated prior preference state or
opinion towards a hypothesis or rated item 𝑖 𝑥on the basis of the past
ratings or experience.
16
17. • Probability of Revising Preference State
• By considering both prior commitment level and social pressure, each agent has a certain
probability to revise the current opinion toward a particular item.
17
19. 19
• Weihua Li, Quan Bai and Minjie Zhang. Comprehensive Influence Propagation Modelling for Hybrid
Social Network, AI2016, Hobart, Australia, 2016
• Weihua Li, Quan Bai and Minjie Zhang, A Multi-agent System for Modelling Preference-Based
Complex Influence Diffusion in Social Networks, The Computer Journal,
https://doi.org/10.1093/comjnl/bxy078
21. • Most existing approaches have been focusing on the diffusion of a single
“message”
• In real world, multiple influences of various topics coexist within the same
context
E.g.: to maximize the influence
of a particular message by
”hiring” seed nodes
Background
22. • Multiple influences of various topics coexist
within the same context and impact each
other
• Different relationships among the influences:
• supportive
• contradictive
• The interactions among the individuals and
influence messages appear complex
Lucy
Sam
The gossip was spread
by the astroturfers
hired by Lucy
Background
23. Agent-based Multiple Influences Diffusion Model (AMID)
• Models the propagation process in a
decentralised manner
• Users are modelled as a set of interactive
agents that possess their own personalised
traits and behaviours
• Influence messages can be interacted with
the agents directly
24. • Relationship 1: User and User
• Users are more likely to be influenced by the
people they know and trust, rather than
from any strangers or systems.
• The trust relationship in this context is
interpreted as truster’s engagement
probability respected to the influence
messages posted by the trustee.
Influential Relationship Modelling
25. • Relationship 2: User and Influence
• Two main factors affecting user’s influence acceptance:
• Peer trust relationships
• Individual’s interests.
Influential Relationship Modelling
26. • Relationship 3: Influence and Influence
• Influences are not capable of interacting
with each other directly, but their relations
and impacts are mediated by user agents.
Influential Relationship Modelling
Influence A
27. • Undesirable influence
• Negative Opinions towards a product
• Scandals
• …
• Traditional Approaches
• Remove Nodes
• Remove Links
• What to do without controls?
Undesirable Influence Minimization
28. Experiments
No Strategies Applied Inject Irrelevant Influence
(seed set size = 20)
Inject Irrelevant Influence
(seed set size = 30)
Inject Relevant Influence
(seed set size = 10)
Inject Opposite Influence
(seed set size = 10)
Inject Opposite Influence
(seed set size = 30)
31. Traditional Recommendation
Systems recommend items to users
• Queries
• Behavior history
• Profile…
• Passively satisfy users’
requirements and demands
• Fail to affect users’ decision-
making when the system
objective conflicts to users’ goals.
40
32. • Behaviors toward social or service
providers’ utility can bring:
• Extra costs
• Extra inconvenience
• Less individual utilities
• Therefore: less attractive
41
34. Proactive recommendation
• Proactive recommendation aims to not only maintain users’
satisfaction, but also realize the system objective.
• One solution: incentivize users
• How to provide incentives or rewards?
43
S. Wu, Q. Bai, and B. H. Kang, “Adaptive Incentive Allocation for
Influence-Aware Proactive Recommendation,” in PRICAI 2019: Trends in
Artificial Intelligence, 2019, pp. 649–661.
35. • Equally provide the rewards:
• If I don’t need, I still don’t need
• Grab the freebie first, then…
• Waste of resources
• Not effective
• How to provide rewards effectively?
• Determine the reward receivers and amount automatically and smartly
• Consider the current context of the environment and different individual
users
44
36. Incentive allocation problem
• To incentivize users with effective incentives
under a budget limitation
• the cost of incentivizing a user is unknown in
advance
• Leaning-based approaches
• Customization-based approaches
• Preferences
• Location
• Skill abilities …
45
37. Influence-aware Incentive allocation problem
• To engage influential users to affect more users’ behaviors in social networks
• The topology of the network is unknown in advance.
• Only the topology is provided
• The strength of influence is unknown
46
38. How about unknown networks?
47
Encode network
information to a low-level
representation
Generate incentive policy
based on state
Observation of users’ behaviors
Incentives allocated to all users
0 1 1 1 0 0 0 0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
1
0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
39. Geometric Actor-Critic (GAC)
• Objective:
• Effectively allocate incentives to users in an unknown
social network, where the knowledge about users’
attributes and strength of influence is unavailable.
• Input
• Two adjacency matrices
• User features matrix (observation of users’ last
behaviors)
• Output
• Incentives allocated to all users
48
40. Experiment
• GAC and its variants are trained 10,000 episodes, with 10 time steps
in every episode by default.
• Three real-world social network datasets
• Compared approaches:
• No incentive
• Uniform allocation
• DGIA-IPE
• DBP-UCB
• Evaluation metric
• the number of users who are incentivized
49
S. Wu, Q. Bai, and B. H. Kang, “Adaptive Incentive Allocation for Influence-Aware Proactive
Recommendation,” in PRICAI 2019: Trends in Artificial Intelligence, 2019, pp. 649–661.
Our research work is based one of the social phenomena: social influence. Social influence occurs when one's emotions, opinions, or behaviours are affected by others. Social influence takes many forms and can be seen in conformity, socialization, peer pressure, obedience, leadership, persuasion, sales and marketing.
One of the typical applications of social influence in the biz field is viral marketing, which aims to direct the market / network to evolve towards a beneficial direction.
Viral marketing relies on the word-of-mouth effect. Information is passing from person to person by oral communications. While, in online social network, WOM can be presented as the information diffusion among the individuals by posting and sharing the innovations. (traditional TV ads are centralized diffusion, while in viral marketing, the influence diffusion among the individuals does not require any cost)
Our research work is based one of the social phenomena: social influence. Social influence occurs when one's emotions, opinions, or behaviours are affected by others. Social influence takes many forms and can be seen in conformity, socialization, peer pressure, obedience, leadership, persuasion, sales and marketing.
One of the typical applications of social influence in the biz field is viral marketing, which aims to direct the market / network to evolve towards a beneficial direction.
Viral marketing relies on the word-of-mouth effect. Information is passing from person to person by oral communications. While, in online social network, WOM can be presented as the information diffusion among the individuals by posting and sharing the innovations. (traditional TV ads are centralized diffusion, while in viral marketing, the influence diffusion among the individuals does not require any cost)
In the contemporary research field, there are two fundamental influence propagation models, which are independent cascade model and linear threshold model. Both models inherit two major features, the propagation and attenuation. The influence is initiated from the activated nodes, and influence diffusion process has been presented as hopping and infecting process. In the meanwhile, this effect decreases when hopping further and further away from the activated nodes. Independent cascade model is non-deterministic, in each hop, there is a certain probability to activate the target successfully. In regards to linear threshold model, it is a deterministic model, each node has a pre-defined threshold, which associates with the individual’s influence acceptance.
Influence Maximization problem is one of the typical application of influence diffusion modelling. It aims to select a limited set of influential users from the social network, hoping that they can propagate positive influence and reach the maximum positive impact across the entire network. The selected users are called seed set, the selection process is named as seed selection. There are some classic seed selection algorithms, such as degree-based selection and greedy selection.
Agent-Based Modelling (ABM): an appropriate approach to explore the macro world through defining micro level of a system.
Users are modelled as autonomous and self-directed agents
Individual’s characters, behaviors can be captured.
From a macroscopic perspective, the ABM demonstrates a decentralized evolutionary pattern driven by the individual’s actions.
Agent-Based Modelling (ABM): an appropriate approach to explore the macro world through defining micro level of a system.
Users are modelled as autonomous and self-directed agents
Individual’s characters, behaviors can be captured.
From a macroscopic perspective, the ABM demonstrates a decentralized evolutionary pattern driven by the individual’s actions.
In Equation \ref{eq:pcl}, $max(R_j) - min(R_j)$ denotes the gap of highest and lowest rating values given by agent $v_j$. While, the $v_j's$ PCL of turning negative is represented as $1 - pcl_{jx}$; The PCL of retaining neutral opinion on $i_x$ is depicted as $1- |pcl_{jx} - 0.5|$.
In Equation \ref{eq:prs_pa}, $prs_{jx} (PA|s_{jx})$ represents the probability of agent $v_j$ to revise the preference state towards $i_x$ from any state to PA. $\lambda_j$ stands for the personalised parameter of $v_j$, which is also a trade-off between the PCL and social pressure. Similarly, Equations \ref{eq:prs_na} and \ref{eq:prs_ia} formulate the probability of revising or retaining the current preference state $s_{jx}$ as NA and IA respectively.
The opinion revision behaviour of an agent is triggered by the update of the neighbour's opinion or the changes of the ratings. Once notified, the agents start the actions.
Trust is abstract which cannot be explicitly calculated, but it can be estimated through by observing the behaviours of two persons. If Person A posts most of the messages from B, that means, A trusts B.
Influence Message 2 is undesirable influence
Ant and stigmergy algorithms leverage the advantages offered from MAS. The ants are modelled as autonomous agents, and they have their own features and behavioral rules but same objective, so that they can achieve group activities, working together to solve a complex problem.
Compared with the traditional agent-based modelling, the ant agents do not pass / exchange the messages directly to their peers, but they communicate with each other indirectly by leaving and sensing a kind of chemical substance on the trails, which is called pheromone.
In the real world, when ants are foraging for food, they left more pheromone closer to the food source, which can be referred by others. In computer science, the ant algorithms are also utilized for optimization problem, such as exploring the shortest path.
===================================
Cellular Automata: A simple and generic ABM is cellular automata (CA), which has been defined as a collection of “coloured” cells on a grid that evolves through a number of discrete time steps according to a set of rules based on the states of neighbouring cells
https://en.wikipedia.org/wiki/Swarm_intelligence
Swarm Intelligence and Ant Colony Optimisation
Multi-agent systems. Many workers coordinate with each other and work on the same problem.
Ant and Stigmergy algorithm is one of the presentation of swarm intelligence, which typically consist of a population of simple agents interacting locally with one another and with their environment.
Stigmergic interaction -> indirect communication
https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms
Inspired by this idea, we model the network as the environment or the working space of the ant agents. Influence diffusion process is modelled as ant crawling behaviours.
Each ant agent walks through the network and leaves the pheromone on the nodes based on its experience. Its local view covers the current node it arrives and the surrounding neighbourhood. The objective of an ant is to explore the potential influential nodes from the environment.
In the meanwhile, ants leave the pheromone on the nodes as messages, which can be referred by their peers.
We will talk about how to select the path and how to allocate the pheromone in the later sections.
Q: Social network seems a global concept -> This just represents the environment of the agent-based model, the individual ant agent’s behaviour does not require a global view, its local view is enough.
Ant: we use a three-tuple
Tour: sequential vector. In this example v_e = v_n, gamma v_e denotes the neighbours of node v_e
Pheromone:
Path selection is one of the ant’s basic behaviours, which describes how a particular ant agent selects the next node to walk when facing multiple choices.
There are two major considerations in regards to path selection. The edge weight and the pheromone amount. The probability for an ant agent walks from v_i to v_j is as formulated as this function.
Explain two figures.
Figure 1: also demonstrates the key idea of path selection. Consider both edge weight and the pheromone amount located on the candidate nodes.
Figure 2: multiple ants crawls in the social network, they do not clash with each other. There can be some overlapping, however, they cannot walk back.
Sub-network generation describes that each ant agent captures a sub-graph after completing a tour. It is the preliminary step of pheromone allocation.
The sub-network contains the nodes in the tour and their first-layer neighbours -> explain the figure on the right. An ant walks through v_1, 2, 3, 4, 5. the first-layer neighbours and the relationships among these nodes are included in this sub-network.
The sub-network generation is also an implication of local influence activation coverage. It measures the assembled influence capability of the sequential nodes in tour. To be more specific, if the tour has fewer nodes but larger sub-network, the more important the tour. Most likely, the potential influential users may reside in this tour.
Pheromone allocation: The pheromone allocation is based on the generated sub-graph. It tells how ants leave the biological information on the nodes that they have completed a tour. Each node in the subnetwork is regarded as one unit of pheromone, and it supposes to contribute the pheromone to the nodes in the tour it linked with. At the same time, the nodes in the tour also contributes pheromones to its neighbourhood in the tour.
Pheromone Evaporation: helps to avoid the convergence to a local optimal solution. The pheromone evaporation is quantified by using this equation, where the amount of pheromone evaporated from each node is associated with the time difference t and the evaporation speed lambda .
As we can see that, ants walk through the network and updates the context by allocating the pheromones. And the seed selection is based on the pheromone amount left on each node, this is pretty much like degree-based approach, but it considers the situation that when two nodes with high degree, but they share many common neighbours, the pheromones are almost averaged in both appears in a tour.
====Seed selection =====
The seed selection in the proposed stigmergy-based approach relies on the amount of pheromone allocated on each node.
The selection is similar to degree-based approach, but it identifies the influential users by ranking the pheromone degree of each node.
Provide information or service based on user query
Learn users preference or interests from historical records
Discovery hidden knowledge and patterns from data