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Unpredictability for different weights assigned to the impact of negative links. The color in the figure represents the unpredictability. Red denotes higher unpredictability and blue denotes higher predictability. https://doi.org/10.1371/journal.pone.0224177.g010
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Information diffusion has been widely discussed in various disciplines including sociology, economics, physics or computer science. In this paper, we generalize the linear threshold model in signed networks consisting of both positive and negative links. We analyze the dynamics of the spread of information based on balance theory, and find that a s...
Citations
... For example, Sueur et al. (2011) showed that heterogeneous groups, characterised by high interindividual variations in network measures, are less efficient to spread information than networks that leaned towards social connections of homogeneous groups. He et al. (2019) introduced a balanced structure on directed networks that could promote the magnitude and speed of information diffusion, eliminate path dependence, and lead to polarisation. ...
Individuals often make decisions in a social environment where social influence can impact on people’s decision-making domains such as online purchasing, political voting and voluntary vaccine uptake. Social influence can be recognised as the intentional or unintentional change in an individual’s belief, perception, or behaviours caused by an information diffusion process embedded in a social network. However, there is limited research on how this diffusion process is shaped by the topology or structure of the social network. This work provides an exploratory and systematic analysis of how decision-making outcomes in a population can be affected by both the structure of the social network and the starting node of where new information starts to diffuse. Simulation results considering three common network structures highlight how social networks with clear community structures lead to a larger absolute impact on decision-making outcomes and networks where the social connections follow a preferential attachment rule show the largest relative impact than the others. The results also suggest scenarios in which introducing new pieces of information to the social network can facilitate the information diffusion process and produce a more significant impact in terms of the overall population decision-making process.
... From a network theory perspective, negative links gave new impetus to study for example novel community detection algorithms (Morrison and Gabbay, 2020;Xia et al., 2021;Zhang et al., 2020). In addition, over the last decade, signed networks and diffusion processes gained new momentum, especially in the field of opinion dynamics (Fan et al., 2012;He et al., 2019;Li et al., 2019;Qu et al., 2021;Shi et al., 2016). ...
... innovation performance and diffusion (Harrigan et al., 2020). To give a brief overview, He et al. (2019) study the diffusion of information through signed networks using a linear threshold model and show that structural balances of negative and positive links can affect information diffusion, spreading ideas through the network more easily. However, they develop the model from an opinion dynamics perspective, enabling the activation of a positive or negative state rather than considering the adoption of innovations and a single activated state. ...
We study the effects of negative links for the innovation diffusion process. To do so we employ an agent-based simulation model of a threshold diffusion process. Our results reveal that negative relationships can have a negative but also positive impact on the innovation diffusion. If network actors simply weigh the positive/negative signals coming from peers that have adopted the innovation, already few negative links will have a significant negative impact on the diffusion performance. If, however, network actors additionally consider the signals coming from peers that have not adopted the innovation, networks with a medium to high number of negative links create a ‘double-negative’ effect which drives the diffusion. Finally, an empirical network shows that our results hold also for real world network data. Our results emphasize the need to better understand how negative relationships, foe effects, and non-adopter effects can hinder or support the diffusion of innovations, potentially explaining why key innovations fail to spread and others succeed. The implications of this research are relevant for the discussion on the diffusion of innovation but also for the diffusion of information, opinions etc. It offers insights for researchers, companies, and policymakers wishing to scale and diffuse innovations more quickly.
... However, the impact of negative relations has been ignored. In reality, interactions among individuals are not only friendly or cooperative relationships but may also be subject to contradictive or defective relationships [43,44]. Firms competing on new standards of innovation may have both opposing and cooperative groups [45]. ...
The community structure in fully signed networks that considers both node attributes and edge signs is important in computational social science; however, its physical description still requires further exploration, and the corresponding measurement remains lacking. In this paper, we present a generalized framework of community structure in fully signed networks, based on which a variant of modularity is designed. An optimization algorithm that maximizes modularity to detect potential communities is also proposed. Experiments show that the proposed method can efficiently optimize the objective function and perform effective community detection.
... The conformity mechanism has also been studied in some signed-network research [52][53][54] in which the principles of "homophily" and "xenophobia" have been employed. Homophily and xenophobia are considered the fundamental roles in shaping signed networks, and they have explained how the clusters form and how polarization appears. ...
... 55,56 The two principles predict that people tend to become more similar to their friends but more different from their enemies. 33,43,[52][53][54] When we introduce the conformity mechanism into the signed-network game, the impact of homophily and xenophobia should be considered. This paper includes the following two conditions in the evolutionary process: (1) behavioral updating without homophily and xenophobia, where an individual learns the same strategy from his neighbors regardless of the relationship signs, and (2) behavioral updating considering homophily and xenophobia, where an individual learns the same strategy from his positive-relation neighbors and the opposite strategy from his negative-relation neighbors. ...
... From the perspective of network structure in the evolutionary process, the structural balance theory plays an important role in predicting the dynamics of signed networks. 52,[52][53][54][57][58][59][60] According to the definition of structural balance, a balanced network can be divided into two clusters, within which people have the same attribute and share positive relationships and between which people have opposite attributes and are connected by negative relationships. 52,59 The logic of structural balance assumes homophily and xenophobia. ...
Human behaviors are often subject to conformity, but little research attention has been paid to social dilemmas in which players are assumed to only pursue the maximization of their payoffs. The present study proposed a generalized prisoner dilemma model in a signed network considering conformity. Simulation shows that conformity helps promote the imitation of cooperative behavior when positive edges dominate the network, while negative edges may impede conformity from fostering cooperation. The logic of homophily and xenophobia allows for the coexistence of cooperators and defectors and guides the evolution toward the equality of the two strategies. We also find that cooperation prevails when individuals have a higher probability of adjusting their relation signs, but conformity may mediate the effect of network adaptation. From a population-wide view, network adaptation and conformity are capable of forming the structures of attractors or repellers.
... We want to examine whether and why sentiment information is more indicative of hierarchy in any particular time period(s). In future work, we also hope to incorporate both positive and negative edges simultaneously in one graph to calculate PageRank, as demonstrated in [11]. ...
Organizational networks are often hierarchical by nature as individuals take on roles or functions at various job levels. Prior studies have used either text-level (e.g., sentiment, affect) or structural-level features (e.g., PageRank, various centrality metrics) to identify influential nodes in networks. In this study, we use a combination of these two levels of information to develop a novel ranking method that combines sentiment analysis and PageRank to infer node-level influence in a real-world organizational network. We detect sentiment scores for all actor pairs based on the content of their email-based communication, and calculate their influence index using an enhanced PageRank method. Finally, we group individual nodes into distinct clusters according to their influence index. Compared to established network metrics designed or used to infer formal and informal influence and ground truth data on job levels, our metric achieves the highest accuracy for inferring formal influence (60.7%) and second highest for inferring informal influence (69.0%). Our approach shows that combining text-level and structural-level information is effective for identifying the job level of nodes in an organizational network.
... directed betweenness centrality (Borgatti et al. 2002)), and there has been a huge amount of research made on signed graphs (e.g. loop sign and structural balance (Cartwright and Harary 1956;Antal et al. 2006); spread of information and social influence (Li et al. 2015;He et al. 2019); and community detection (Traag and Bruggeman 2009;Esmailian and Jalili 2015)), it is only until recently that methods measuring node centrality for networks of signed interactions start to emerge. For instance, Bonacich and Lloyd (2004) developed a status measure by using eigenvector, where a high status node usually has many positive relations with other members of the same clique and negative relations with members of other cliques. ...
... Second, effects such as social influence and sentiments can spread among individuals (Fowler and Christakis 2008;Chan et al. 2018); and following the logic behind our first assumption mentioned above, it is not unreasonable to consider the effect of i on j via k as the product of two probabilities, namely the effect of i on k and the effect of k on j. Third, basing on the notion of transitivity and structural balance, which has been frequently studied on signed social networks (Cartwright and Harary 1956;Du et al. 2016;He et al. 2019), if the effect of i on k and the effect of k on j are of the same signs, then their product, or namely the effect of i on j via k, should be positive; whereas if the effect of i on k and the effect of k on j are of the opposite signs, then the effect of i on j via k should be negative. With those assumptions in mind, the basic idea behind our approach is to consider how positive and negative effects from a node i can propagate through a network. ...
Abstract The position of a node in a social network, or node centrality, can be quantified in several ways. Traditionally, it can be defined by considering the local connectivity of a node (degree) and some non-local characteristics (distance). Here, we present an approach that can quantify the interaction structure of signed digraphs and we define a node centrality measure for these networks. The basic principle behind our approach is to determine the sign and strength of direct and indirect effects of one node on another along pathways. Such an approach allows us to elucidate how a node is structurally connected to other nodes in the social network, and partition its interaction structure into positive and negative components. Centrality here is quantified in two ways providing complementary information: total effect is the overall effect a node has on all nodes in the same social network; while net effect describes, whether predominately positive or negative, the manner in which a node can exert on the social network. We use Sampson’s like-dislike relation network to demonstrate our approach and compare our result to those derived from existing centrality indices. We further demonstrate our approach by using Hungarian school classroom social networks.
Social influence has been widely discussed in various disciplines due to its important sociological significance. However, the dynamics of social influence in signed networks have nonetheless received fairly little attention. In this article, we propose a generalized Pólya urn model that considers the effect of negative relationships and is capable of comprehending the specific mechanisms of homophily and xenophobia in the dynamics. Based on the mathematical deduction, we find that the signed network guides social influence in a trend toward equality. Simulation shows that a higher effect or larger proportion of negative relationships may break the self-reinforcement and make the market more equal. The collective dynamics in the signed network are more predictable but generate path dependence. We also find that a balanced structure has no impact on the average market share but is helpful in removing path dependence and promoting system stability.
Network-based interventions have shown immense potential in prompting behaviour changes in populations. Their implementation in the real world however, is often difficult and prone to failure as they are typically delivered on limited budgets and in many instances can be met with resistance in populations. Therefore, utilising available and limited resources optimally through careful and efficient planning is key for the successful implementation of any intervention. An important development in this aspect, is the influence maximisation framework —which lies at the interface of network science and computer science —and is commonly used to study network-based interventions in a theoretical setup with the aim of determining best practices that can optimise intervention outcomes in the real world. In this thesis, we explore the influence maximisation problem in a competitive setting (inspired by real-world conditions) where two contenders compete to maximise the spread of their intervention (or influence) in a social network. In its traditional form, the influence maximisation process identifies the k most influential nodes in a network —where k is given by a fixed budget. In this thesis, we propose the influence maximisation model with continuous distribution of influence where individuals are targeted heterogeneously based on their role in the influence spread process. This approach allows policymakers to obtain a detailed plan of the optimal distribution of budgets which is otherwise abstracted in traditional methods. In the rest of the thesis we use this approach to study multiple real-world settings. We first propose the competitive influence maximisation model with continuous allocation of resources. We then determine optimal intervention strategies against known competitor allocations in a network and show that continuous distribution of resources consistently outperform traditional approaches where influence is concentrated on a few nodes in the network (i.e. k optimal nodes). We further extend the model to a game-theoretic framework which helps us examine settings with no prior information about competitor strategies. We find that the equilibrium solution in this setting is to uniformly target the network —implying that all nodes, irrespective of their topological positions, contribute equally to the influence maximisation process. We extend this model further in two directions. First, we introduce the notion of adoption barriers to the competitive influence maximisation model, where an additional cost is paid every time an individual is approached for intervention. We find that this cost-of-access parameter ties our model to traditional methods, where only k individuals are discretely targeted. We further generalise the model to study other real-world settings where the strength of influence changes nonlinearly with allocations. Here we identify two distinct regimes —one where optimal strategies offer significant gains, and the other where they do not yield any gains. The two regimes also vary in their sensitivity to budget availability, and we find that in some cases, even a tenfold increase in the budget only marginally improves the outcome of the intervention. Second, we extend the continuous allocation model to analyse network-based interventions in the presence of negative ties. Individuals sharing a negative tie typically influence each other to adopt opposing views, and hence they can be detrimental to the influence spread process if not considered in the dynamics. We show that in general it is important to consider negative ties when planning an intervention, and at the same time we identify settings where the knowledge of negative ties yields no gains, or leads to less favourable outcomes.
