334 research outputs found
Modelling Non-Linear Consensus Dynamics on Hypergraphs
The basic interaction unit of many dynamical systems involves more than two
nodes. In such situations where networks are not an appropriate modelling
framework, it has recently become increasingly popular to turn to higher-order
models, including hypergraphs. In this paper, we explore the non-linear
dynamics of consensus on hypergraphs, allowing for interactions within
hyperedges of any cardinality. After discussing the different ways in which
non-linearities can be incorporated in the dynamical model, building on
different sociological theories, we explore its mathematical properties and
perform simulations to investigate them numerically. After focussing on
synthetic hypergraphs, namely on block hypergraphs, we investigate the dynamics
on real-world structures, and explore in detail the role of involvement and
stubbornness on polarisation
Pinning Control of Hypergraphs
A standard assumption in control of network dynamical systems is that its nodes interact through pairwise interactions, which can be described by means of a directed graph. However, in several contexts, multibody, directed interactions may occur, thereby requiring the use of directed hypergraphs rather then digraphs. For the first time, we propose a strategy, inspired by the classic pinning control on graphs, that is tailored for controlling network systems coupled through a directed hypergraph. By drawing an analogy with signed graphs, we provide sufficient conditions for controlling the network onto the desired trajectory provided by the pinner, and a dedicated algorithm to design the control hyperedges
Dynamical systems on hypergraphs
We present a general framework that enables one to model high-order
interaction among entangled dynamical systems, via hypergraphs. Several
relevant processes can be ideally traced back to the proposed scheme. We shall
here solely elaborate on the conditions that seed the spontaneous emergence of
patterns, spatially heterogeneous solutions resulting from the many-body
interaction between fundamental units. In particular we will focus, on two
relevant settings. First, we will assume long-ranged mean field interactions
between populations, and then turn to considering diffusive-like couplings. Two
applications are presented, respectively to a generalised Volterra system and
the Brusselator model
Pinning control of hypergraphs
A standard assumption in control of network dynamical systems is that its nodes interact through pairwise interactions, which can be described by means of a directed graph. However, in several contexts, multibody, directed interactions may occur, thereby requiring the use of directed hypergraphs rather then digraphs. For the first time, we propose a strategy, inspired by the classic pinning control on graphs, that is tailored for controlling network systems coupled through a directed hypergraph. By drawing an analogy with signed graphs, we provide sufficient conditions for controlling the network onto the desired trajectory provided by the pinner, and a dedicated algorithm to design the control hyperedges
Evolution of honesty in higher-order social networks
Sender-receiver games are simple models of information transmission that provide a formalism to study the evolution of honest signaling and deception between a sender and a receiver. In many practical scenarios, lies often affect groups of receivers, which inevitably entangles the payoffs of individuals to the payoffs of other agents in their group, and this makes the formalism of pairwise sender-receiver games inapt for where it might be useful the most. We therefore introduce group interactions among receivers and study how their interconnectedness in higher-order social networks affects the evolution of lying. We observe a number of counterintuitive results that are rooted in the complexity of the underlying evolutionary dynamics, which has thus far remained hidden in the realm of pairwise interactions. We find conditions for honesty to persist even when there is a temptation to lie, and we observe the prevalence of moral strategy profiles even when lies favor the receiver at a cost to the sender. We confirm the robustness of our results by further performing simulations on hypergraphs created from real-world data using the SocioPatterns database. Altogether, our results provide persuasive evidence that moral behavior may evolve on higher-order social networks, at least as long as individuals interact in groups that are small compared to the size of the network
Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction
Advances in deep neural network (DNN) architectures have enabled new
prediction techniques for stock market data. Unlike other multivariate
time-series data, stock markets show two unique characteristics: (i)
\emph{multi-order dynamics}, as stock prices are affected by strong
non-pairwise correlations (e.g., within the same industry); and (ii)
\emph{internal dynamics}, as each individual stock shows some particular
behaviour. Recent DNN-based methods capture multi-order dynamics using
hypergraphs, but rely on the Fourier basis in the convolution, which is both
inefficient and ineffective. In addition, they largely ignore internal dynamics
by adopting the same model for each stock, which implies a severe information
loss.
In this paper, we propose a framework for stock movement prediction to
overcome the above issues. Specifically, the framework includes temporal
generative filters that implement a memory-based mechanism onto an LSTM network
in an attempt to learn individual patterns per stock. Moreover, we employ
hypergraph attentions to capture the non-pairwise correlations. Here, using the
wavelet basis instead of the Fourier basis, enables us to simplify the message
passing and focus on the localized convolution. Experiments with US market data
over six years show that our framework outperforms state-of-the-art methods in
terms of profit and stability. Our source code and data are available at
\url{https://github.com/thanhtrunghuynh93/estimate}.Comment: Technical report for accepted paper at WSDM 202
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