KICSS2020 Invited Talk 2: Prof. Quan Bai from University of Tasmania
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Invited Talk 2: Agent-based Influence Propagation Modelling: Methos and Applications Prof. Quan Bai University of Tasmania
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KICSS2020 Invited Talk 2: Prof. Quan Bai from University of Tasmania
- 1. Agent-based Influence Propagation Modelling: Methods and Applications A/Prof. Quan Bai School of Information and Communication Technologies University of Tasmania Australia 1
- 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
- 3. 3 Tasmania
- 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
- 18. 18 • Result: Scenario 1: PA>NA Scenario 2: NA>PA Scenario 3: PA=NA, and no IA
- 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)
- 29. Experiments Undesirable Influence (Cumulative AICD) Injected Influence (Cumulative AICD) • Weihua Li, Quan Bai, Minjie Zhang and Tung Doan Nguyen, Modelling Multiple Influences Diffusion in On-line Social Networks, AAMAS 2018
- 30. 39 Influence based incentive allocation for proactive recommendations (application)
- 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
- 33. 42 Fail to change users’ decisions
- 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.
- 43. • Influence propagation modelling is important and challenging: • Distributed environment • Computational overhead • Dynamic environment • Distributed influence propagation models can contribute. • AIDM: agent-based simulation and modelling • AMID: agent-based multiple influences diffusion modelling • Proactive recommendation (by utilising influences) Summary 52
- 44. 53 Dr. Weihua Li Dr. Yi Yang Shiqing Wu My team: Yuxuan Hu Renjie Li Naimeng Yao Lirui Tian Xiang Li Chenting Jiang Hong Liang
Editor's Notes
- 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