AI Can Enhance Creativity in Social Networks

• URL: http://arxiv.org/abs/2410.15264v2
• Date: Mon, 28 Oct 2024 20:31:21 GMT
• Title: AI Can Enhance Creativity in Social Networks
• Authors: Raiyan Abdul Baten, Ali Sarosh Bangash, Krish Veera, Gourab Ghoshal, Ehsan Hoque,
• Abstract summary: We trained a model that predicts people's ideation performances using semantic and network-structural features. SocialMuse maximizes people's predicted performances to generate peer recommendations for them. We found treatment networks leveraging SocialMuse outperformed AI-agnostic control networks in several creativity measures.
• Score: 1.8317588605009203
• License:
• Abstract: Can peer recommendation engines elevate people's creative performances in self-organizing social networks? Answering this question requires resolving challenges in data collection (e.g., tracing inspiration links and psycho-social attributes of nodes) and intervention design (e.g., balancing idea stimulation and redundancy in evolving information environments). We trained a model that predicts people's ideation performances using semantic and network-structural features in an online platform. Using this model, we built SocialMuse, which maximizes people's predicted performances to generate peer recommendations for them. We found treatment networks leveraging SocialMuse outperforming AI-agnostic control networks in several creativity measures. The treatment networks were more decentralized than the control, as SocialMuse increasingly emphasized network-structural features at large network sizes. This decentralization spreads people's inspiration sources, helping inspired ideas stand out better. Our study provides actionable insights into building intelligent systems for elevating creativity.

Related papers

• Modeling Social Media Recommendation Impacts Using Academic Networks: A Graph Neural Network Approach [4.138915764680197]
  This study proposes to use academic social networks as a proxy for investigating recommendation systems in social media. By employing Graph Neural Networks (GNNs), we develop a model that separates the prediction of academic infosphere from behavior prediction. Our approach aims to improve our understanding of recommendation systems' roles and social networks modeling.
  arXiv  Detail & Related papers  (2024-10-06T17:03:27Z)
• Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
  Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces. While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks. This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
  arXiv  Detail & Related papers  (2024-04-29T04:51:05Z)
• Link Prediction for Social Networks using Representation Learning and Heuristic-based Features [1.279952601030681]
  Predicting missing links in social networks efficiently can help in various modern-day business applications. Here, we explore various feature extraction techniques to generate representations of nodes and edges in a social network.
  arXiv  Detail & Related papers  (2024-03-13T15:23:55Z)
• Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks [63.246437631458356]
  Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native. This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
  arXiv  Detail & Related papers  (2023-09-23T00:05:39Z)
• A Brief Review of Hypernetworks in Deep Learning [16.82262394666801]
  Hypernetworks are neural networks that generate weights for another neural network, known as the target network. We present an illustrative example of training deep neural networks using hypernets and propose categorizing hypernets based on five design criteria. We discuss the challenges and future directions that remain underexplored in the field of hypernets.
  arXiv  Detail & Related papers  (2023-06-12T08:37:38Z)
• Exploiting Social Graph Networks for Emotion Prediction [2.7376140293132667]
  We utilize mobile sensing data to predict happiness and stress. In addition to a person's physiological features, we also incorporate the environment's impact through weather and social network. We propose an architecture that automates the integration of a user's social network affect prediction.
  arXiv  Detail & Related papers  (2022-07-12T20:24:39Z)
• Learning with Capsules: A Survey [73.31150426300198]
  Capsule networks were proposed as an alternative approach to Convolutional Neural Networks (CNNs) for learning object-centric representations. Unlike CNNs, capsule networks are designed to explicitly model part-whole hierarchical relationships.
  arXiv  Detail & Related papers  (2022-06-06T15:05:36Z)
• Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks [0.734084539365505]
  Predictions are typically based on unsupervised or supervised learning. We argue that richer information about personal social structure of individuals might lead to better predictions. We show that social-awareness generally provides significant improvements in the prediction performance.
  arXiv  Detail & Related papers  (2021-09-19T18:49:10Z)
• A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning [54.55119659523629]
  Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Common-pool resources include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere.
  arXiv  Detail & Related papers  (2020-10-15T14:12:26Z)
• I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance [79.05613148641018]
  We will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance.
  arXiv  Detail & Related papers  (2020-02-05T11:10:44Z)
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation [50.08581302050378]
  Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences. We propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet) In this paper, we propose DiffNet++, an improved algorithm of Diffnet that models the neural influence diffusion and interest diffusion in a unified framework.
  arXiv  Detail & Related papers  (2020-01-15T08:45:34Z)

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.