【導(dǎo)讀】社會網(wǎng)絡(luò)在實地考察研究中出現(xiàn)的問題。節(jié)討論了社會網(wǎng)絡(luò)研究中使用的代理模型。本節(jié)開頭試圖解釋相關(guān)領(lǐng)域的研究人員所定義的復(fù)雜性。結(jié)尾部分簡要說明了最近復(fù)雜網(wǎng)絡(luò)理論在多學(xué)科的應(yīng)?，F(xiàn)實世界的很多應(yīng)用網(wǎng)絡(luò)規(guī)模龐大，在被稱為“復(fù)雜網(wǎng)絡(luò)”下產(chǎn)生了多學(xué)科研究的新。會科學(xué)的多種學(xué)科。在過去十年里，對復(fù)雜網(wǎng)絡(luò)的研究取得了非凡的進步。經(jīng)解決了網(wǎng)絡(luò)特征的識別及其相應(yīng)的解決措施。洞穴項目的關(guān)鍵是“解決復(fù)雜性”，這可能有助于提出該項目目標的概念。復(fù)雜網(wǎng)絡(luò)的這個概念到現(xiàn)在已經(jīng)存在了。將近10年,我們在隨后的一節(jié)討論。其中最早嘗試研究這些所謂“復(fù)雜網(wǎng)絡(luò)”的行為追溯到隨機圖理論的開創(chuàng)性的工作，情況下，人們發(fā)現(xiàn)，仿真結(jié)果生成的數(shù)據(jù)具有肥尾和薄高峰;被稱為leptokurtosis特點。這進一步鼓勵了在統(tǒng)計簽名。行為為徹底調(diào)查高度準候選人提供分析。的相互作用,影響著整個系統(tǒng)所有成員的相互作用、相互影響。代理商低于其門檻（或臨界）規(guī)定的很多的時間。

　　

【正文】 ion that differentiate one group from another.” al. remends, it is really important to interview as many as possible people in the casestudy areas, as people39。s views about their acquaintances and their groups help in identifying the inclusion/exclusion, closeness/openness within and among the groups. Returning to the notion of neighborhood , physical neighbors maybe described in terms of the land use, segregation of subregions etc. while the social aspect of neighborhood is based on the 39。local social interactions, social class, ethnic and radical origins, life cycle characteristics of the population, length of residence, and place of work39。 etc. A focus of any evidencebased research is the munity , a social unit, where people share mon 39。values and needs39。 and/or similar ideologies. As the concept of locality is embedded in its definition, a munity may be identified as sharing social characteristics or as munity space , where the interactions take place, or both. Local interaction among neighbors has been a subject of quite a few agentbased social simulations, and as we 15 shall discuss later on, a number of such simulation models are inherently spatially natural unit in a munity space is the household or a cluster of households, eg in extended families etc. Identifying the munity space and distinct regions is vital, especially in the context of modeling landuse changes etc. (LUCC, 2020) As Bailey and Gatrell (1995) explain, “spatial data analysis is involved when data are spatially located and explicit consideration is given to the possible importance of their spatial arrangement in the analysis or interpretation of results.” Spatial analysis, for example those based on GIS (Graphical Information Systems) techniques highlight the importance, provided it exists, of neighborhood or influences, if any, in the actors39。 behavior caused by the spatialcontext. 。 Schensul et al. (1999) has thoroughly covered the issues involving spatial mapping of data。 we however, restrict to reporting a few most relevant points. For any social works, the atomic units are obviously the individuals. In gathering data about individuals, it is quite useful to identify the general spatiotemporal constraints that limit most individuals39。 movements and interaction in the region. An important facet of a munity, imminent for investigating social works, is the existence of socalled 39。munity anizations39。, which operate within the perimeters of the munity. Such anizations maybe identified in terms of their membership types (eg open, by reference, etc.), the services they offer (eg social, economical, etc.) and the minimum threshold of the number of members, and other factors. Such anizations can be statemanaged, nongovernment anizations (NGOs), or looselycoupled local clubs etc. For public or stateowned anizations, which usually serve for the general welfare of the inhabitants in a munity, it is relatively easier to obtain data, than otherwise. In case of the local private clubs, dedicated to specific interests of their members, acquiring data is certainly a nontrivial task as people usually avoid disclosing their private social activities and affiliations. However, with respect to the social works in the munity, such local clubs are the building block and hence are most important to be investigated. A major difficulty in this regard is the local people39。s mistrust to the fieldwork researchers, especially if the neighborhood is hardpressed and poor. As the effects resulting form a qualitative research in social works are not visible immediately, people tend to think such practices as waste of time and perhaps exploitation. It is therefore, of immense importance, that the researchers involved in qualitative social works fieldwork, are supported with sufficient time and funds, so that they maybe able to gain the confidence of the local people, by staying and involving them as well. An interesting methodology involving the 39。players39。, is the setting up of 39。roleplaying games39。, which we briefly review later. 4 .4 Towards NonTraditional SNA Recently, the social work analysis munity has been concerned over the use (and misuse) of metrics and validation techniques for both the empirical and simulated works. With the plethora of available measurement tools, it has bee immensely important to choose carefully the most suitable set of metrics depending upon the context of the underlying study. For example, whether it is about the spread of disease, 16 asymmetric information exchange, dyadic friendships relations etc。 especially, when identifying the key players in case of transmission. Hence whether the data are directed or undirected, the nature of the work and the context are important. Batti and Mehra (draft) have recently addressed the four major criticisms on the traditional social work analysis. We present only their assessment related to dynamics and 39。agency39。, briefly。 details can be found in the original traditional structuralist approach has invited the criticism that work research is static and thus only the oute are focused, and ignoring the factors that have caused evolution of the work. Nevertheless, with respect to the notion diffusion of information and the social impact, it is not only supported by strong theoretical basis, but also these concepts have led to the development of quite a few analytical and graphical analysis tools. References ALBERT, R.,and Barabasi, A.(2020), Statistical Mechanics of Complex Networks, available at ALBERT, R., and A. Barab225。si (2020), Statistical Mechanics of Complex Networks, Rev. Mod. Phys. , 74, 47, available at AXELROD, R. (1997), Advancing the Art of Simulation in the Social Science, in Conte, Hengselmann and Terno (eds.), Simulating Social Phenomena, Berlin et al: SpringerVerlag. AXTELL, RL (2020), 39。E