78,042 research outputs found
Dynamical Stability of Threshold Networks over Undirected Signed Graphs
In this paper we study the dynamic behavior of threshold networks on
undirected signed graphs. While much attention has been given to the
convergence and long-term behavior of this model, an open question remains: How
does the underlying graph structure influence network dynamics? While similar
papers have been carried out for threshold networks (as well as for other
networks) these have largely focused on unsigned networks. However, the signed
graph model finds applications in various real-world domains like gene
regulation and social networks.
By studying a graph parameter that we call "stability index," we search to
establish a connection between the structure and the dynamics of threshold
network. Interestingly, this parameter is related to the concepts of
frustration and balance in signed graphs. We show that graphs that present
negative stability index exhibit stable dynamics, meaning that the dynamics
converges to fixed points regardless of threshold parameters. Conversely, if at
least one subgraph has positive stability index, oscillations in long term
behavior may appear. Finally, we generalize the analysis to network dynamics
under periodic update schemes and we explore the case in which the stability
index is positive for some subgraph finding that attractors with
superpolynomial period on the size of the network may appear
Balancing Augmentation with Edge-Utility Filter for Signed GNNs
Signed graph neural networks (SGNNs) has recently drawn more attention as
many real-world networks are signed networks containing two types of edges:
positive and negative. The existence of negative edges affects the SGNN
robustness on two aspects. One is the semantic imbalance as the negative edges
are usually hard to obtain though they can provide potentially useful
information. The other is the structural unbalance, e.g. unbalanced triangles,
an indication of incompatible relationship among nodes. In this paper, we
propose a balancing augmentation method to address the above two aspects for
SGNNs. Firstly, the utility of each negative edge is measured by calculating
its occurrence in unbalanced structures. Secondly, the original signed graph is
selectively augmented with the use of (1) an edge perturbation regulator to
balance the number of positive and negative edges and to determine the ratio of
perturbed edges to original edges and (2) an edge utility filter to remove the
negative edges with low utility to make the graph structure more balanced.
Finally, a SGNN is trained on the augmented graph which effectively explores
the credible relationships. A detailed theoretical analysis is also conducted
to prove the effectiveness of each module. Experiments on five real-world
datasets in link prediction demonstrate that our method has the advantages of
effectiveness and generalization and can significantly improve the performance
of SGNN backbones.Comment: 16 page
Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
The problem of representing nodes in a signed network as low-dimensional
vectors, known as signed network embedding (SNE), has garnered considerable
attention in recent years. While several SNE methods based on graph
convolutional networks (GCN) have been proposed for this problem, we point out
that they significantly rely on the assumption that the decades-old balance
theory always holds in the real-world. To address this limitation, we propose a
novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect
embedding propagation in GCN by utilizing the trustworthiness on edge signs for
high-order relationships inferred by the balance theory. The proposed approach
consists of three modules: (M1) generation of each node's extended ego-network;
(M2) measurement of trustworthiness on edge signs; and (M3)
trustworthiness-aware propagation of embeddings. Furthermore, TrustSGCN learns
the node embeddings by leveraging two well-known societal theories, i.e.,
balance and status. The experiments on four real-world signed network datasets
demonstrate that TrustSGCN consistently outperforms five state-of-the-art
GCN-based SNE methods. The code is available at
https://github.com/kmj0792/TrustSGCN.Comment: 12 pages, 8 figures, 9 table
Emergent Behaviors over Signed Random Networks in Dynamical Environments
We study asymptotic dynamical patterns that emerge among a set of nodes that
interact in a dynamically evolving signed random network. Node interactions
take place at random on a sequence of deterministic signed graphs. Each node
receives positive or negative recommendations from its neighbors depending on
the sign of the interaction arcs, and updates its state accordingly. Positive
recommendations follow the standard consensus update while two types of
negative recommendations, each modeling a different type of antagonistic or
malicious interaction, are considered. Nodes may weigh positive and negative
recommendations differently, and random processes are introduced to model the
time-varying attention that nodes pay to the positive and negative
recommendations. Various conditions for almost sure convergence, divergence,
and clustering of the node states are established. Some fundamental
similarities and differences are established for the two notions of negative
recommendations
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