2,339 research outputs found
Complex influence propagation based on trust-aware dynamic linear threshold models
Abstract To properly capture the complexity of influence propagation phenomena in real-world contexts, such as those related to viral marketing and misinformation spread, information diffusion models should fulfill a number of requirements. These include accounting for several dynamic aspects in the propagation (e.g., latency, time horizon), dealing with multiple cascades of information that might occur competitively, accounting for the contingencies that lead a user to change her/his adoption of one or alternative information items, and leveraging trust/distrust in the users' relationships and its effect of influence on the users' decisions. To the best of our knowledge, no diffusion model unifying all of the above requirements has been developed so far. In this work, we address such a challenge and propose a novel class of diffusion models, inspired by the classic linear threshold model, which are designed to deal with trust-aware, non-competitive as well as competitive time-varying propagation scenarios. Our theoretical inspection of the proposed models unveils important findings on the relations with existing linear threshold models for which properties are known about whether monotonicity and submodularity hold for the corresponding activation function. We also propose strategies for the selection of the initial spreaders of the propagation process, for both non-competitive and competitive influence propagation tasks, whose goal is to mimic contexts of misinformation spread. Our extensive experimental evaluation, which was conducted on publicly available networks and included comparison with competing methods, provides evidence on the meaningfulness and uniqueness of our models
XFlow: Benchmarking Flow Behaviors over Graphs
The occurrence of diffusion on a graph is a prevalent and significant
phenomenon, as evidenced by the spread of rumors, influenza-like viruses, smart
grid failures, and similar events. Comprehending the behaviors of flow is a
formidable task, due to the intricate interplay between the distribution of
seeds that initiate flow propagation, the propagation model, and the topology
of the graph. The study of networks encompasses a diverse range of academic
disciplines, including mathematics, physics, social science, and computer
science. This interdisciplinary nature of network research is characterized by
a high degree of specialization and compartmentalization, and the cooperation
facilitated by them is inadequate. From a machine learning standpoint, there is
a deficiency in a cohesive platform for assessing algorithms across various
domains. One of the primary obstacles to current research in this field is the
absence of a comprehensive curated benchmark suite to study the flow behaviors
under network scenarios.
To address this disparity, we propose the implementation of a novel benchmark
suite that encompasses a variety of tasks, baseline models, graph datasets, and
evaluation tools. In addition, we present a comprehensive analytical framework
that offers a generalized approach to numerous flow-related tasks across
diverse domains, serving as a blueprint and roadmap. Drawing upon the outcomes
of our empirical investigation, we analyze the advantages and disadvantages of
current foundational models, and we underscore potential avenues for further
study. The datasets, code, and baseline models have been made available for the
public at: https://github.com/XGraphing/XFlo
Maximizing Welfare in Social Networks under a Utility Driven Influence Diffusion Model
Motivated by applications such as viral marketing, the problem of influence
maximization (IM) has been extensively studied in the literature. The goal is
to select a small number of users to adopt an item such that it results in a
large cascade of adoptions by others. Existing works have three key
limitations. (1) They do not account for economic considerations of a user in
buying/adopting items. (2) Most studies on multiple items focus on competition,
with complementary items receiving limited attention. (3) For the network
owner, maximizing social welfare is important to ensure customer loyalty, which
is not addressed in prior work in the IM literature. In this paper, we address
all three limitations and propose a novel model called UIC that combines
utility-driven item adoption with influence propagation over networks. Focusing
on the mutually complementary setting, we formulate the problem of social
welfare maximization in this novel setting. We show that while the objective
function is neither submodular nor supermodular, surprisingly a simple greedy
allocation algorithm achieves a factor of of the optimum
expected social welfare. We develop \textsf{bundleGRD}, a scalable version of
this approximation algorithm, and demonstrate, with comprehensive experiments
on real and synthetic datasets, that it significantly outperforms all
baselines.Comment: 33 page
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