Reconstructing signed relations from interaction data

• URL: http://arxiv.org/abs/2209.03219v1
• Date: Wed, 7 Sep 2022 15:23:51 GMT
• Title: Reconstructing signed relations from interaction data
• Authors: Georges Andres, Giona Casiraghi, Giacomo Vaccario, Frank Schweitzer
• Abstract summary: Despite their importance, data about signed relations is rare and commonly gathered through surveys. In this paper, we show how the underlying signed relations can be extracted with such data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Positive and negative relations play an essential role in human behavior and shape the communities we live in. Despite their importance, data about signed relations is rare and commonly gathered through surveys. Interaction data is more abundant, for instance, in the form of proximity or communication data. So far, though, it could not be utilized to detect signed relations. In this paper, we show how the underlying signed relations can be extracted with such data. Employing a statistical network approach, we construct networks of signed relations in four communities. We then show that these relations correspond to the ones reported in surveys. Additionally, the inferred relations allow us to study the homophily of individuals with respect to gender, religious beliefs, and financial backgrounds. We evaluate the importance of triads in the signed network to study group cohesion.

Related papers

• Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction [121.65152276851619]
  We show that semantic correlations between relations are inherently edge-level and entity-independent. We propose a novel subgraph-based method, namely TACO, to model Topology-Aware COrrelations between relations. To further exploit the potential of RCN, we propose Complete Common Neighbor induced subgraph.
  arXiv  Detail & Related papers  (2023-09-20T08:11:58Z)
• Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions [49.305189190372765]
  Social relation information can play an assistant role in the (dis)agreement task besides textual information. We propose a novel method to extract such relation information from (dis)agreement data into an inductive social relation graph. We find social relations can boost the performance of the (dis)agreement detection model, especially for the long-token comment-reply pairs.
  arXiv  Detail & Related papers  (2023-02-08T09:09:47Z)
• Analyzing Wikipedia Membership Dataset and PredictingUnconnected Nodes in the Signed Networks [0.666659730119789]
  We examine how relationships can be predicted between two unconnected people in a social network by using area under Precison-Recall curve and ROC. Modeling the social network as a signed graph, we compare Triadic model,Latent Information model and Sentiment model and use them to predict peer to peer interactions.
  arXiv  Detail & Related papers  (2021-10-18T09:03:18Z)
• Link Prediction on N-ary Relational Data Based on Relatedness Evaluation [61.61555159755858]
  We propose a method called NaLP to conduct link prediction on n-ary relational data. We represent each n-ary relational fact as a set of its role and role-value pairs. Experimental results validate the effectiveness and merits of the proposed methods.
  arXiv  Detail & Related papers  (2021-04-21T09:06:54Z)
• A Machine Learning Approach to Predicting Continuous Tie Strengths [0.4014524824655105]
  Relationships between people constantly evolve, altering interpersonal behavior and defining social groups. Relationships between nodes in social networks can be represented by a tie strength, often empirically assessed using surveys. We propose a system that allows for the continuous approximation of relationships as they evolve over time.
  arXiv  Detail & Related papers  (2021-01-23T05:01:05Z)
• Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction [84.64435075778988]
  We propose a general approach to learn relation prototypes from unlabeled texts. We learn relation prototypes as an implicit factor between entities. We conduct experiments on two publicly available datasets: New York Times and Google Distant Supervision.
  arXiv  Detail & Related papers  (2020-11-27T06:21:12Z)
• Skeleton-based Relational Reasoning for Group Activity Analysis [40.49389173100578]
  We leverage the skeleton information to learn the interactions between the individuals straight from it. Our experiments demonstrate the potential of skeleton-based approaches for modeling multi-person interactions.
  arXiv  Detail & Related papers  (2020-11-11T09:25:53Z)
• Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training [49.9995628166064]
  We propose CTEG, a model equipped with two mechanisms to learn to decouple easily-confused relations. On the one hand, an EGA mechanism is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations.
  arXiv  Detail & Related papers  (2020-10-21T11:07:53Z)
• Social Science Guided Feature Engineering: A Novel Approach to Signed Link Analysis [58.892336054718825]
  Most existing work on link analysis focuses on unsigned social networks. The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks. Recent findings suggest that properties of signed networks substantially differ from those of unsigned networks.
  arXiv  Detail & Related papers  (2020-01-04T00:26:05Z)

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.