SEARCH · Searcharxiv

Results for “physics.soc-ph”

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,099 records · Page 61Linked to original sources

arXiv2014

The Precautionary Principle (with Application to the Genetic Modification of Organisms)

We present a non-naive version of the Precautionary (PP) that allows us to avoid paranoia and paralysis by confining precaution to specific domains and problems. PP is intended to deal with uncertainty and risk in cases where the absence of evidence and the incompleteness of scientific knowledge carries profound implications and in the presence of risks of "black swans", unforeseen and unforeseable events of extreme consequence. We formalize PP, placing it within the statistical and probabilistic structure of ruin problems, in which a system is at risk of total failure, and in place of risk we use a formal fragility based approach. We make a central distinction between 1) thin and fat tails, 2) Local and systemic risks and place PP in the joint Fat Tails and systemic cases. We discuss the implications for GMOs (compared to Nuclear energy) and show that GMOs represent a public risk of global harm (while harm from nuclear energy is comparatively limited and better characterized). PP should be used to prescribe severe limits on GMOs.

q-fin.GN↗

arXiv2014

Discovering Organizational Correlations from Twitter

Organizational relationships are usually very complex in real life. It is difficult or impossible to directly measure such correlations among different organizations, because important information is usually not publicly available (e.g., the correlations of terrorist organizations). Nowadays, an increasing amount of organizational information can be posted online by individuals and spread instantly through Twitter. Such information can be crucial for detecting organizational correlations. In this paper, we study the problem of discovering correlations among organizations from Twitter. Mining organizational correlations is a very challenging task due to the following reasons: a) Data in Twitter occurs as large volumes of mixed information. The most relevant information about organizations is often buried. Thus, the organizational correlations can be scattered in multiple places, represented by different forms; b) Making use of information from Twitter collectively and judiciously is difficult because of the multiple representations of organizational correlations that are extracted. In order to address these issues, we propose multi-CG (multiple Correlation Graphs based model), an unsupervised framework that can learn a consensus of correlations among organizations based on multiple representations extracted from Twitter, which is more accurate and robust than correlations based on a single representation. Empirical study shows that the consensus graph extracted from Twitter can capture the organizational correlations effectively.

cs.SI↗

arXiv2014

The Probabilistic Structure of Discrete Agent-Based Models

This paper describes a formalization of agent-based models (ABMs) as random walks on regular graphs and relates the symmetry group of those graphs to a coarse-graining of the ABM that is still Markovian. An ABM in which $N$ agents can be in $δ$ different states leads to a Markov chain with $δ^N$ states. In ABMs with a sequential update scheme by which one agent is chosen to update its state at a time, transitions are only allowed between system configurations that differ with respect to a single agent. This characterizes ABMs as random walks on regular graphs. The non-trivial automorphisms of those graphs make visible the dynamical symmetries that an ABM gives rise to because sets of micro configurations can be interchanged without changing the probability structure of the random walk. This allows for a systematic loss-less reduction of the state space of the model.

cs.MA↗

arXiv2014

Regularities in the dynamics and development of the International System

A finite-time singularity accompanied by log-periodic oscillations shaped the war dynamics and development of the International System during the period 1495 - 1945. The identification of this singularity provides us with a perspective to penetrate and decode the dynamics of the International System. Various regularities in the dynamics of the International System can be identified. These regularities are remarkably consistent, and can be attributed to the connectivity and the growth of connectivity of the International System.

physics.soc-ph↗

arXiv2018

A Unified Framework for Community Detection and Network Representation Learning

Network representation learning (NRL) aims to learn low-dimensional vectors for vertices in a network. Most existing NRL methods focus on learning representations from local context of vertices (such as their neighbors). Nevertheless, vertices in many complex networks also exhibit significant global patterns widely known as communities. It's intuitive that vertices in the same community tend to connect densely and share common attributes. These patterns are expected to improve NRL and benefit relevant evaluation tasks, such as link prediction and vertex classification. Inspired by the analogy between network representation learning and text modeling, we propose a unified NRL framework by introducing community information of vertices, named as Community-enhanced Network Representation Learning (CNRL). CNRL simultaneously detects community distribution of each vertex and learns embeddings of both vertices and communities. Moreover, the proposed community enhancement mechanism can be applied to various existing NRL models. In experiments, we evaluate our model on vertex classification, link prediction, and community detection using several real-world datasets. The results demonstrate that CNRL significantly and consistently outperforms other state-of-the-art methods while verifying our assumptions on the correlations between vertices and communities.

cs.SI↗

arXiv2017

Weighted H-index for identifying influential spreaders

Spreading is a ubiquitous process in the social, biological and technological systems. Therefore, identifying influential spreaders, which is important to prevent epidemic spreading and to establish effective vaccination strategies, is full of theoretical and practical significance. In this paper, a weighted h-index centrality based on virtual nodes extension is proposed to quantify the spreading influence of nodes in complex networks. Simulation results on real-world networks reveal that the proposed method provides more accurate and more consistent ranking than the five classical methods. Moreover, we observe that the monotonicity and the computational complexity of our measure can also yield excellent performance.

physics.soc-ph↗

arXiv2017

Statistical validation of financial time series via visibility graph

Statistical physics of complex systems exploits network theory not only to model, but also to effectively extract information from many dynamical real-world systems. A pivotal case of study is given by financial systems: market prediction represents an unsolved scientific challenge yet with crucial implications for society, as financial crises have devastating effects on real economies. Thus, nowadays the quest for a robust estimator of market efficiency is both a scientific and institutional priority. In this work we study the visibility graphs built from the time series of several trade market indices. We propose a validation procedure for each link of these graphs against a null hypothesis derived from ARCH-type modeling of such series. Building on this framework, we devise a market indicator that turns out to be highly correlated and even predictive of financial instability periods.

q-fin.RM↗

arXiv2017

Anomalous popularity growth in social tagging ecosystems

In social tagging systems, the diversity of tag vocabulary and the popularity of such tags continue to increase as they are exposed to selection pressure derived from our cognitive nature and cultural preferences. This is analogous to living ecosystems, where mutation and selection play a dominant role. Such population dynamism, which yields a scaling law, is mathematically modeled by a simple stochastic process---the Yule--Simon process, which describes how new words are introduced to the system and then grow. However, in actual web services, we have observed that a large fluctuation emerges in the popularity growth of individual tags that cannot be explained by the ordinary selection mechanism. We introduce a scaling factor to quantify the degree of the deviation in the popularity growth from the mean-field solution of the Yule--Simon process, and we discuss possible triggers of such anomalous popularity behavior.

physics.soc-ph↗

arXiv2017

Analysis of the U.S. Patient Referral Network

In this paper we analyze the US Patient Referral Network (also called the Shared Patient Network) and various subnetworks for the years 2009--2015. In these networks two physicians are linked if a patient encounters both of them within a specified time-interval, according to the data made available by the Centers for Medicare and Medicaid Services. We find power law distributions on most state-level data as well as a core-periphery structure. On a national and state level, we discover a so-called small-world structure as well as a "gravity law" of the type found in some large-scale economic networks. Some physicians play the role of hubs for interstate referral. Strong correlations between certain network statistics with healthcare system statistics at both the state and national levels are discovered. The patterns in the referral network evinced using several statistical analyses involving key metrics derived from the network illustrate the potential for using network analysis to provide new insights into the healthcare system and opportunities or mechanisms for catalyzing improvements.

cs.SI↗

arXiv2017

Distributed Rumor Blocking with Multiple Positive Cascades

Misinformation and rumor can spread rapidly and widely through online social networks and therefore rumor controlling has become a critical issue. It is often assumed that there is a single authority whose goal is to minimize the spread of rumor by generating a positive cascade. In this paper, we study a more realistic scenario when there are multiple positive cascades generated by different agents. For the multiple-cascade diffusion, we propose the P2P independent cascade (PIC) model for private social communications. The main part of this paper is an analysis of the rumor blocking effect (i.e. the number of the users activated by rumor) when the agents non-cooperatively generate the positive cascades. We show that the rumor blocking effect provided by the Nash equilibrium will not be arbitrarily worse even if the positive cascades are generated non-cooperatively. In addition, we give a discussion on how the cascade priority and activation order affect the rumor blocking problem. We experimentally examine the Nash equilibrium of the proposed games by simulations done on real social network structures.

cs.SI↗

arXiv2017

An Analytical Framework for Understanding the Intensity of Religious Fundamentalism

This paper examines the process of emergence of religious fundamentalism through development parameters. Therefore this research work reflects an analytical discussion on how the level of religious fundamentalism can be explained by the economic, political administrative and legal parameters such as GDP, Employment to Population ratio, Government Effectiveness, Voice & Accountability, Rule of Law (World Justice Project Report) and Rule of law (Governance Indicators).

physics.soc-ph↗

arXiv2018

SaC2Vec: Information Network Representation with Structure and Content

Network representation learning (also known as information network embedding) has been the central piece of research in social and information network analysis for the last couple of years. An information network can be viewed as a linked structure of a set of entities. A set of linked web pages and documents, a set of users in a social network are common examples of information network. Network embedding learns low dimensional representations of the nodes, which can further be used for downstream network mining applications such as community detection or node clustering. Information network representation techniques traditionally use only the link structure of the network. But in real world networks, nodes come with additional content such as textual descriptions or associated images. This content is semantically correlated with the network structure and hence using the content along with the topological structure of the network can facilitate the overall network representation. In this paper, we propose Sac2Vec, a network representation technique that exploits both the structure and content. We convert the network into a multi-layered graph and use random walk and language modeling technique to generate the embedding of the nodes. Our approach is simple and computationally fast, yet able to use the content as a complement to structure and vice-versa. We also generalize the approach for networks having multiple types of content in each node. Experimental evaluations on four real world publicly available datasets show the merit of our approach compared to state-of-the-art algorithms in the domain.

cs.SI↗

arXiv2018

Noise-driven bias in the non-local voter model

Is it more effective to have a strong influence over a small domain, or a weaker influence over a larger one? Here, we introduce and analyse an off-lattice generalisation of the voter model, in which the range and strength of agents' influence are control parameters. We consider both low and high density regimes and, using distinct mathematical approaches, derive analytical predictions for the evolution of agent densities. We find that, even when the agents are equally persuasive on average, those whose influence is wider but weaker have an overall noise-driven advantage allowing them to reliably dominate the entire population. We discuss the implications of our results and the potential of our model (or adaptations thereof) to improve the understanding of political campaign strategies and the evolution of disease.

physics.soc-ph↗

arXiv2018

Emergent Open-Endedness from Contagion of the Fittest

In this paper, we study emergent irreducible information in populations of randomly generated computable systems that are networked and follow a "Susceptible-Infected-Susceptible" contagion model of imitation of the fittest neighbor. We show that there is a lower bound for the stationary prevalence (or average density of "infected" nodes) that triggers an unlimited increase of the expected local emergent algorithmic complexity (or information) of a node as the population size grows. We call this phenomenon expected (local) emergent open-endedness. In addition, we show that static networks with a power-law degree distribution following the Barabási-Albert model satisfy this lower bound and, thus, display expected (local) emergent open-endedness.

cs.SI↗

arXiv2018

A partial differential equations approach to defeating partisan gerrymandering

We introduce a novel partial differential equations approach for addressing the problem of partisan gerrymandering. Our method is based on volume preserving curvature flow, a partial differential equation which we adapt to smooth voting district boundaries while preserving equal voting populations. We show that every step of the flow minimizes a "compactness energy", allowing us to demonstrate that our method produces more "compact" and reasonable district maps. We compute the flow using a variant of "auction dynamics" --- an efficient MBO type algorithm for computing volume preserving curvature flows. This "auction dynamics" approach can be used to generate hundreds of reasonable maps in a matter of seconds without parallelization. The compactness energy provides a way of comparing proposed districtings of a given state. We demonstrate both the map generation and map comparison features of our approach for several different states.

physics.soc-ph↗

arXiv2018

The evolving networks of debtor-creditor relationships with addition and deletion of nodes: a case of P2P lending

P2P lending activities have grown rapidly and have caused the huge and complex networks of debtor-creditor relationships. The aim of this study was to study the underlying structural characteristics of networks formed by debtor-creditor relationships. According attributes of P2P lending, this paper model the networks of debtor-creditor relationships as an evolving networks with addition and deletion of nodes. It was found that networks of debtor-creditor relationships are scale-free networks. Moreover, the exponent of power-law was calculated by an empirical study. In addition, this paper study what factors impact on the exponent of power-law besides the number of nodes. It was found that the both interest rate and term have significantly influence on the exponent of power-law. Interest rate is negatively correlated with the exponent of power-law and term is positively correlated with the exponent of power-law. Our results enriches the application of complex networks

q-fin.GN↗

arXiv2018

Using Core-Periphery Structure to Predict High Centrality Nodes in Time-Varying Networks

Vertices with high betweenness and closeness centrality represent influential entities in a network. An important problem for time varying networks is to know a-priori, using minimal computation, whether the influential vertices of the current time step will retain their high centrality, in the future time steps, as the network evolves. In this paper, based on empirical evidences from several large real world time varying networks, we discover a certain class of networks where the highly central vertices are part of the innermost core of the network and this property is maintained over time. As a key contribution of this work, we propose novel heuristics to identify these networks in an optimal fashion and also develop a two-step algorithm for predicting high centrality vertices. Consequently, we show for the first time that for such networks, expensive shortest path computations in each time step as the network changes can be completely avoided; instead we can use time series models (e.g., ARIMA as used here) to predict the overlap between the high centrality vertices in the current time step to the ones in the future time steps. Moreover, once the new network is available in time, we can find the high centrality vertices in the top core simply based on their high degree.

cs.SI↗

arXiv2018

Team dynamics during the delivery of a large-scale, engineered system

Coordinated collective action refers to the synchronized action of agents towards achieving a predefined set of goals. Such activity is at the core of a wide range of social challenges, from organizational dynamics to team performance. Focusing on the latter, a novel dataset that captures the planned efforts to deliver a large-scale, engineered system is introduced. In detail, this dataset is composed of a total of 271 unique individuals, responsible for the delivery of a total of 721 tasks spread across a period of 745 days. The focus of this analysis is on the collaboration network between individuals, captured by their co-assignment in the delivery of particular tasks, and their dynamical patterns. Results indicate that the delivery of some tasks depends on disproportionately large collaborations, making them intrinsically harder to manage compared to tasks which depend on small, or no, collaborations. Similarly, some tasks require a disproportionately diverse set of skills to be completed, further enhancing their intrinsic complexity. Shifting focus to the topology of the contribution network, an abrupt emergence, and subsequent contraction, of a single large cluster is observed. This phenomenon corresponds to the emergence of an increasingly large and cohesive team, and its subsequent decomposition. In addition, the evolution of this cluster tightly follows the number of active tasks, suggesting that large teams are a natural way to respond to increased workload. These findings provide new insight on the underlying team dynamics that govern coordinated collective activity in general, and in the context of project delivery specifically.

physics.soc-ph↗