Interactions in information spread: quantification and interpretation using stochastic block models

• URL: http://arxiv.org/abs/2004.04552v3
• Date: Tue, 1 Feb 2022 16:31:33 GMT
• Title: Interactions in information spread: quantification and interpretation using stochastic block models
• Authors: Ga\"el Poux-M\'edard, Julien Velcin, Sabine Loudcher
• Abstract summary: In social networks, users' behavior results from the people they interact with, news in their feed, or trending topics. Here, we propose a new model, the Interactive Mixed Membership Block Model (IMMSBM), which investigates the role of interactions between entities. In inference tasks, taking them into account leads to average relative changes with respect to non-interactive models of up to 150% in the probability of an outcome.
• Score: 3.5450828190071655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In most real-world applications, it is seldom the case that a given observable evolves independently of its environment. In social networks, users' behavior results from the people they interact with, news in their feed, or trending topics. In natural language, the meaning of phrases emerges from the combination of words. In general medicine, a diagnosis is established on the basis of the interaction of symptoms. Here, we propose a new model, the Interactive Mixed Membership Stochastic Block Model (IMMSBM), which investigates the role of interactions between entities (hashtags, words, memes, etc.) and quantifies their importance within the aforementioned corpora. We find that interactions play an important role in those corpora. In inference tasks, taking them into account leads to average relative changes with respect to non-interactive models of up to 150\% in the probability of an outcome. Furthermore, their role greatly improves the predictive power of the model. Our findings suggest that neglecting interactions when modeling real-world phenomena might lead to incorrect conclusions being drawn.

Related papers

• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development. We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems [70.84976977950075]
  Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time. We propose a novel model that captures the continuous interaction among agents using a Graph Neural Network (GNN) as the ODE function. The key innovation of our model is to learn time-dependent representations of treatments and incorporate them into the ODE function, enabling precise predictions of potential outcomes.
  arXiv  Detail & Related papers  (2024-02-29T23:07:07Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• HIINT: Historical, Intra- and Inter- personal Dynamics Modeling with Cross-person Memory Transformer [38.92436852096451]
  Cross-person memory Transformer (CPM-T) framework is able to explicitly model affective dynamics. CPM-T framework maintains memory modules to store and update the contexts within the conversation window. We evaluate the effectiveness and generalizability of our approach on three publicly available datasets for joint engagement, rapport, and human beliefs prediction tasks.
  arXiv  Detail & Related papers  (2023-05-21T06:43:35Z)
• DIDER: Discovering Interpretable Dynamically Evolving Relations [14.69985920418015]
  This paper introduces DIDER, Discovering Interpretable Dynamically Evolving Relations, a generic end-to-end interaction modeling framework with intrinsic interpretability. We evaluate DIDER on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-08-22T20:55:56Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• A Probabilistic Model Of Interaction Dynamics for Dyadic Face-to-Face Settings [1.9544213396776275]
  We develop a probabilistic model to capture the interaction dynamics between pairs of participants in a face-to-face setting. This interaction encoding is then used to influence the generation when predicting one agent's future dynamics. We show that our model successfully delineates between the modes, based on their interacting dynamics.
  arXiv  Detail & Related papers  (2022-07-10T23:31:27Z)
• Multi-Agent Imitation Learning with Copulas [102.27052968901894]
  Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
  arXiv  Detail & Related papers  (2021-07-10T03:49:41Z)
• Divide and Rule: Recurrent Partitioned Network for Dynamic Processes [25.855428321990328]
  Many dynamic processes are involved with interacting variables, from physical systems to sociological analysis. Our goal is to represent a system with a part-whole hierarchy and discover the implied dependencies among intra-system variables. The proposed architecture consists of (i) a perceptive module that extracts a hierarchical and temporally consistent representation of the observation at multiple levels, (ii) a deductive module for determining the relational connection between neurons at each level, and (iii) a statistical module that can predict the future by conditioning on the temporal distributional estimation.
  arXiv  Detail & Related papers  (2021-06-01T06:45:56Z)
• Information Interaction Profile of Choice Adoption [2.9972063833424216]
  We introduce an efficient method to infer the entities interaction network and its evolution according to the temporal distance separating interacting entities. The interaction profile allows characterizing the mechanisms of the interaction processes. We show that the effect of a combination of exposures on a user is more than the sum of each exposure's independent effect--there is an interaction.
  arXiv  Detail & Related papers  (2021-04-28T10:42:25Z)
• Causal Discovery in Physical Systems from Videos [123.79211190669821]
  Causal discovery is at the core of human cognition. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure.
  arXiv  Detail & Related papers  (2020-07-01T17:29:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.