Frédéric Amblard

An Individual‐Based Model of Innovation Diffusion Mixing Social Value and Individual Benefit1

The authors propose an individual‐based model of innovation diffusion and explore its main dynamical properties. In the model, individuals assign an a priori social value to an innovation which evolves during their interactions with the “relative agreement” influence model. This model offers the possibility of including a minority of “extremists” with extreme and very definite opinions. Individuals who give a high social value to the innovation tend to look for information that allows them to evaluate more precisely the individual benefit of adoption. If the social value they assign is low, they neither consider the information nor transmit it. The main finding is that innovations with high social value and low individual benefit have a greater chance of succeeding than innovations with low social value and high individual benefit. Moreover, in some cases, a minority of extremists can have a very important impact on the propagation by polarizing the social value.

Detection of Overlapping Communities in Dynamical Social Networks

Community detection on networks is a well-known problem encountered in many fields, for which the existing algorithms are inefficient 1) at capturing overlaps in-between communities, 2) at detecting communities having disparities in size and density 3) at taking into account the networks’ dynamics. In this paper, we propose a new algorithm (iLCD) for community detection using a radically new approach. Taking into account the dynamics of the network, it is designed for the detection of strongly overlapping communities. We first explain the main principles underlying the iLCD algorithm, introducing the two notions of intrinsic communities and longitudinal detection, and detail the algorithm. Then, we illustrate its efficiency in the case of a citation network, and then compare it with existing most efficient algorithms using a standard generator of community-based networks, the LFR benchmark.

Interacting Agents and Continuous Opinions Dynamics

We present a model of opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. High thresholds yield convergence of opinions towards an average opinion, whereas low thresholds result in several opinion clusters. The model is further generalised to threshold heterogeneity, adaptive thresholds and binary strings of opinions.

Simulate to Detect: A Multi-agent System for Community Detection

Community detection in social networks is a well-known problem encountered in many fields. Many traditional algorithms have been proposed to solve it, with recurrent problems: impossibility to deal with dynamic networks, sensitivity to noise, no detection of overlapping communities, exponential running time. This paper proposes a multi-agent system that replays the evolution of a network and, in the same time, reproduces the rise and fall of communities. After presenting the strengths and weaknesses of existing community detection algorithms, we describe the multi-agent system we propose. Then, we compare our solution with existing works, and show some advantages of our method, in particular the possibility to dynamically detect the communities.

Automated community detection on social networks: useful? efficient? asking the users

In most online social networks, with the increasing number of users and content, the problem of contact filtering becomes more and more present. The recent introduction of such features in online social networks -- for instance, Circles in Google+ or Facebook Smart lists -- shows that it is a problem they are confronted to. In this paper, we explore this question through multidisciplinary aspects. First, we discuss about this issue of groups management in the context of social networks. Then, we present several techniques from the state of the art to automatically find meaningful groups of contacts in a users contact list. Finally, we asked Facebook users to evaluate these solutions on their own Facebook network, both to compare the solutions among themselves and to assess how pertinent the best ones are according to them. The conclusions of this study is that a network analysis approach can strongly improve the efficiency of an automated detection of groups on networks, which could be used, combined with profile data extraction, to design intelligent management of groups of contacts.

Social Media-Based Collaborative Information Access: Analysis of Online Crisis-Related Twitter Conversations

The notion of implicit (or explicit) collaborative information access refers to systems and practices allowing a group of users to unintentionally (respectively intentionally) seek, share and retrieve information to achieve similar (respectively shared) information-related goals. Despite an increasing adoption in social environments, collaboration behavior in information seeking and retrieval is mainly limited to small-sized groups, generally restricted to working spaces. Much remains to be learned about collaborative information seeking within open web social spaces. This paper is an attempt to better understand either implicit or explicit collaboration by studying Twitter, one of the most popular and widely used social networks. We study in particular the complex intertwinement of human interactions induced by both collaboration and social networking. We empirically explore explicit collaborative interactions based on focused conversation streams during two crisis. We identify structural patterns of temporally representative conversation subgraphs and represent their topics using Latent Dirichlet Allocation (LDA) modeling. Our main findings suggest that: 1) the critical mass of collaboration is generally limited to small-sized flat networks, with or without an influential user, 2) users are active as members of weakly overlapping groups and engage in numerous collaborative search and sharing tasks dealing with different topics, and 3) collaborative group ties evolve within the time-span of conversations.

The MAELIA Multi-Agent Platform for Integrated Analysis of Interactions Between Agricultural Land-Use and Low-Water Management Strategies

The MAELIA project is developing an agent-based modeling and simulation platform to study the environmental, economic and social impacts of various regulations regarding water use and water management in combination with climate change. It is applied to the case of the French Adour-Garonne Basin, which is the most concerned in France by water scarcity during the low-water period. An integrated approach has been chosen to model this social-ecological system: the model combines spatiotemporal models of ecologic (e.g. rainfall and temperature changes, water flow and plant growth) and socio-economic (e.g. farmer decision-making process, management of low-water flow, demography, land use and land cover changes) processes and sub-models of cognitive sharing among agents (e.g. weather forecast, normative constraints on behaviors)

Dynamics of Relative Agreement in Multiple Social Contexts

In real world scenarios, the formation of consensus is an self-organisation process by which actors have to make a joint assessment about a target subject being it a decision making problem or the formation of a collective opinion. In social simulation, models of opinion dynamics tackle the opinion formation phenomena. These models try to make an assessment, for instance, of the ideal conditions that lead an interacting group of agents to opinion consensus, polarisation or fragmentation. In this paper, we investigate the role of social relation structure in opinion dynamics using an interaction model of relative agreement. We present an agent-based model that defines social relations as multiple concomitant social networks and apply our model to an opinion dynamics model with bounded confidence. We discuss the influence of complex social network topologies where actors interact in multiple relations simultaneously. The paper builds on previous work about social space design with multiple contexts and context switching, to determine the influence of such complex social structures in a process such as opinion formation.

Social Networks and Spatial Distribution

In most agent-based social simulation models, the issue of the organisation of the agents’ population matters. The topology, in which agents interact, be it spatially structured or a social network, can have important impacts on the obtained results in social simulation. Unfortunately, the necessary data about the target system is often lacking; therefore, you have to use models in order to reproduce realistic spatial distributions of the population and/or realistic social networks among the agents. In this chapter, we identify the main issues concerning this point and describe several models of social networks or of spatial distribution that can be integrated in agent-based simulation to go a step forwards from the use of a purely random model. In each case, we identify several output measures that allow quantifying their impacts.

An Asynchronous Double Auction Market to Study the Formation of Financial Bubbles and Crashes

Stock market is a complex system composed from heterogeneous traders with highly non-linear interactions from which emerge a phenomenon of speculative bubble. To understand the role of heterogeneous behaviors of traders and interactions between them in the emergence of bubbles, we propose an agent-based model of double auction market, with asynchronous time management, where traders act asynchronously and take different times to make decisions. The market is populated by heterogeneous traders. In addition to fundamentalist, noise, and technical (chartist) traders, we propose a hybrid trader, which can switch between technical (chartist) and fundamentalist strategies integrating panicking behavior. We find that when market is populated by a majority of hybrid traders, we observe quite realistic bubble formation characterized by a boom phase when hybrid traders switch to technical behavior, followed by a relatively shorter burst phase when hybrid traders return to fundamentalist strategy and change to panicked state. The aim is to design agents which act asynchronously, with simple behaviors, but complex enough to produce realistic price dynamics, which provide a basis for developing agents with sophisticated decision-making processes.