Finding Early Adopters of Innovation in Social Networks Balázs R. Sziklai12and Balázs Lengyel13

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Finding Early Adopters of Innovation in

Social Networks

Balázs R. Sziklai 1,2 and Balázs Lengyel 1,3

1Centre for Economic and Regional Studies, Budapest, Hungary

2Department of Operations Research and Actuarial Sciences,

Corvinus University of Budapest

3Corvinus Institute for Advanced Studies, Corvinus University of

Budapest

Abstract

Social networks play a fundamental role in the diﬀusion of inno-

vation through peers’ inﬂuence on adoption. Thus, network position

including a wide range of network centrality measures have been used

to describe individuals’ aﬃnity to adopt an innovation and their ability

to propagate diﬀusion. Yet, social networks are assortative in terms

of susceptibility and inﬂuence and in terms of network centralities as

well. This makes the identiﬁcation of inﬂuencers diﬃcult especially

since susceptibility and centrality does not always go hand in hand.

Here we propose the Top Candidate algorithm, an expert recommen-

dation method, to rank individuals based on their perceived expertise,

which resonates well with the assortative nature of innovators and

early adopters. Leveraging adoption data from two online social net-

works that are assortative in terms of adoption but represent diﬀerent

levels of assortativity of network centralities, we demonstrate that the

Top Candidate ranking is more eﬃcient in capturing early adopters

than other widely used indices. Top Candidate nodes adopt earlier

and have higher reach among innovators, early adopters and early ma-

jority than nodes highlighted by other methods. These results suggest

that the Top Candidate method can identify good seeds for inﬂuence

maximization campaigns on social networks.

arXiv:2210.13907v1 [cs.SI] 25 Oct 2022

Keywords: Online social networks, Innovation adoption, Network central-

ity measures,Top Candidate ranking, Homophily

1 Introduction

Most individuals adopt an innovation by imitating their inﬂuential peers

(Rogers, 1962; Bass, 1969) that underlines the role of social networks in the

diﬀusion of new products, technologies or ideas (Granovetter, 1978; Valente,

1996). Network scientists argue that the structure of social networks can

explain the underlying mechanisms of social inﬂuence and adoption: highly

connected nodes have more inﬂuence than others (Pastor-Satorras et al.,

2015), while diﬀusion is more likely in tightly connected cliques and less

likely across them (Centola and Macy, 2007). Wang et al. (2019) paint a

more nuanced picture as they found that although simple messages spread

eﬀectively via network hubs, for complex stories, the inﬂuence of ’ordinary

people’ (individuals that are less connected) is more important.

A central part of this discussion has led to the "inﬂuence maximization"

problem (IM) (Kempe et al., 2003), which aims to identify the ideal seed

nodes that a marketing campaign should target to achieve maximum impact,

given pre-deﬁned diﬀusion models. The IM is NP-hard; thus, many use

heuristics to ﬁnd the seed nodes and start optimization by assuming that

nodes with high network centrality (e.g. degree) are inﬂuential spreaders

(Kitsak et al., 2010; De Arruda et al., 2014) and run diﬀusion simulations,

most notably using the linear threshold and the independent cascade models.

However, these models fail to capture an important feature that is observed

in real life networks: homophily, the tendency that similar individuals are

more likely to be connected than dissimilar ones.

Homophily, also referred to as assortativity in relation with social net-

works (Newman, 2002), is a general phenomenon (McPherson et al., 2001;

Cho et al., 2012) that has a fundamental role in innovation spreading (Anwar

et al., 2021). Ignoring this eﬀect poses a major problem for seed indentiﬁ-

cation in inﬂuence maximization when the sole source of information is the

network structure (Aral and Dhillon, 2018). Social inﬂuence and central-

ity are diﬃcult to disentangle without knowing at least some of the early

adopters of the speciﬁc innovation (Banerjee et al., 2013; Toole et al., 2012).

There are plenty of reasons why a central individual may be reluctant to par-

ticipate in a campaign or may not be susceptible to the marketing message.

The most prominent one is risk-averseness. Subscribing to a new trend or

technology needs commitment and entails social risk – not everyone is willing

to do that. Central agents with many friends may particularly feel the social

pressure to be conformist and to avoid eccentric behavior. Innovators and

early adopters, on the other hand, are known to possess psychological traits

that makes them perfect subject for the early market of an innovation.

In this paper, we aim to contribute to this discussion in two ways. First,

empirical data on adoption dynamics from two online social networks enable

us to investigate how network structure can be useful to identify innovators

and early adopters in innovation diﬀusion. Second, we propose a ranking

of the users based on the so-called Top Candidate method (Sziklai, 2018) –

an expert selection algorithm that exhibits features resembling assortativity.

We argue that (i) these contributions together provide new heuristics for

seed selection in inﬂuence maximization (ii) any framework that neglects

assortativity of social networks when testing heuristics is prone to produce

inaccurate predictions.

We compare the Top Candidate ranking with seven well-known centrality

measures on two online social networks: iWiW from Hungary and Pokec

from Slovakia. Registration days of users are known in both networks, both

are assortative in terms of adoption time but represent diﬀerent levels of

assortativity in network centralities. We look at the top 1000 nodes of the

Top Candidate ranking and the other seven alternative measures and plot

how the date of registration is distributed over time.

We ﬁnd that the Top Candidate ranking is more eﬃcient in capturing

innovators and early adopters than other widely used indicators. Top Can-

didate nodes adopt earlier and have higher reach among innovators, early

adopters and early majority than nodes highlighted by other methods. These

results suggest that the Top Candidate method can identify good seeds for

inﬂuence maximization campaigns on social networks.

2 Literature overview

2.1 Early adopters as experts

The identiﬁcation of innovators and early adopters is key for marketing cam-

paigns and their characterization received considerable attention. The lit-

erature converges toward the conclusion that innovators and early adopters

stand out from their peers.

Rogers (2003) describes innovators as venturesome individuals who can

cope with a high degree of uncertainty, and early adopters as a group with

high socio-economic status. Moore (2014) depicts innovators as technology

enthusiasts, or geeks and early adopters as visionaries who are willing to take

high risk.

A ﬁeld study by Brancheau and Wetherbe (1990) supports hypotheses

that early adopters were more highly educated, more attuned to mass media,

more involved in interpersonal communication, and more likely to be opinion

leaders. Eastlick and Lotz (1999) reports that social risk negatively relates to

the tendency to be a potential innovator and potential innovators possessed

signiﬁcantly stronger opinion leadership. A dutch survey shows that early

adopters are likely to be highly mobile, have a high socio-economic status,

high levels of education and high personal incomes (Zijlstra et al., 2020).

Finally, Muller and Yogev (2006) provides empirical evidence that the average

time at which the main market outnumbers the early market is indeed when

16% of the market has already adopted the product – giving support Rogers

(1962)’s somewhat arbitrary (or rather inspired) division of adopter sets.

Another important concept is market mavenness (Feick and Price, 1987).

Market mavens are consumers who are highly involved in a market. They

have information about many kinds of products and shops, and they enjoy

sharing their knowledge. Peers often seek out their opinion and rely on

their expertise. Goldsmith et al. (2003) ﬁnds that consumer innovativeness

and market mavenism positively correlates, although they argue that market

mavens and innovators are distinct groups.

Directly related to the context of this study, Lynn et al. (2011) explores

the relationship between personality traits of early adopters of social network

sites. They report that extraversion, openness and conscientiousness impact

positively and signiﬁcantly on information sharing, and negatively on rumor

sharing. On the other hand both, information sharing and rumor sharing

impact positively and signiﬁcantly on the centrality of early adopters. The

seemingly contradictory observations, can be explained away by separating

the social status of opinion leadership and the inﬂuencing capacity of the

agent which relates more to network centrality.

To sum up, innovators and early adopters stand out in their personal char-

acteristics. Thus, marketing campaigns have usually targeted and labelled

them as experts to convince society. However, both Lynn et al. (2011) and

Dedehayir et al. (2017) argue that a distinction has to be made between opin-

ion leadership and innovativeness. Even Rogers (2003) aﬃrms that opinion

leaders are not necessarily innovators.

2.2 Early Adoption and homophily in network diﬀusion

In the Inﬂuence Maximization framework (Kempe et al., 2003), few papers

addressed other node characteristics concentrating in network communities

that can help to predict the future popularity of novelty. For example, inﬂu-

ential individuals can form clusters that can help the early propagation of an

idea (Aral and Walker, 2012). Weng et al. (2014) build a predictive model for

meme popularity using three classes of features: network topology, commu-

nity diversity, and growth rate. They found that community related features

are the most powerful predictors of future success. Hajdu et al. (2020) study

the community structure of public transportation networks and ﬁnds that

transmission probabilities depend on the community structure. Calió and

Tagarelli (2021) study attribute based seed diversiﬁcation. They argue that

a seed set with diﬀerent characteristics (age, gender, etc.) might be more

successful in information-propagation. Rahimkhani et al. (2015) identiﬁes

the community structures of the input graph then chooses a number of rep-

resentative nodes to form the ﬁnal output of the proposed algorithm.

However, this literature has largely overlooked a phenomenon inherent is

social networks and diﬀusion dynamics alike: the role of homophily (McPher-

son et al., 2001). It has long been recognized that a behavior can spread in

society only when those most prone to it are surrounded by peers who are

somewhat less but almost equally open to it’s adoption (Granovetter, 1978).

In other words, innovators must be connected to early adopters such that

adoption can penetrate in their communities and later inﬂuence the rest

of the market too, otherwise the innovation will not spread (Watts, 2002).

Adoption dynamics can be predicted at small scales only by assuming ho-

mophily of adoption (Toole et al., 2012). Despite the importance of adoption

homophily in networks, it has been largely ignored in inﬂuence maximization

modeling (Aral and Dhillon, 2018).

Instead, a usual assumption to ﬁnd the seed nodes for Inﬂuence Maxi-

mization is that network structure alone can quantify inﬂuence. For example,

nodes with high network centrality (e.g. degree) are usually considered as in-

ﬂuential spreaders (Kitsak et al., 2010; De Arruda et al., 2014).

Finally, the presence of assortativity implies that not every connection is

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FindingEarlyAdoptersofInnovationinSocialNetworksBalázsR.Sziklai1,2andBalázsLengyel1,31CentreforEconomicandRegionalStudies,Budapest,Hungary2DepartmentofOperationsResearchandActuarialSciences,CorvinusUniversityofBudapest3CorvinusInstituteforAdvancedStudies,CorvinusUniversityofBudapestAbstractSocialnet...

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Finding Early Adopters of Innovation in Social Networks Balázs R. Sziklai12and Balázs Lengyel13

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