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The adoption of generative Artificial Intelligence (GAI) in organizational settings calls into question workers' roles, and relatedly, the implications for their long-term skill development and domain expertise. In our qualitative study in the software engineering domain, we build on the theoretical lenses of occupational identity and self-determination theory to understand how and why software engineers make sense of GAI for their work. We find that engineers' sense-making is contingent on domain expertise, as juniors and seniors felt their needs for competence, autonomy, and relatedness to be differently impacted by GAI. We shed light on the importance of the individual's role in preserving tacit domain knowledge as engineers engaged in sense-making that protected their occupational identity. We illustrate how organizations play an active role in shaping workers' sense-making process and propose design guidelines on how organizations and system designers can facilitate the impact of technological change on workers' occupational identity.

The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process - the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice., Comment: Conditionally Accepted to CHI24. 27 pages

Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other important aspects were overlooked. Various airborne pathogens differ in the duration of exposure needed for infection. Also, from the individual perspective, Covid-19 progression differs between individuals, and its severity is statistically correlated with age. Here, we enrich an interaction-driven model of Covid-19 and similar airborne viral diseases with (a) meetings duration and (b) personal disease progression. The enriched model enables predicting outcomes at both the population and the individual levels. It further allows predicting individual risk of engaging in social interactions as a function of the virus characteristics and its prevalence in the population. We further showed that the enigmatic nature of asymptomatic transmission stems from the latent effect of the network density on this transmission and that asymptomatic transmission has a substantial impact only in sparse communities.

The temporal dynamics of social interactions were shown to influence the spread of disease. Here, we model the conditions of progression and competition for several viral strains, exploring various levels of cross-immunity over temporal networks. We use our interaction-driven contagion model and characterize, using it, several viral variants. Our results, obtained on temporal random networks and on real-world interaction data, demonstrate that temporal dynamics are crucial to determining the competition results. We consider two and three competing pathogens and show the conditions under which a slower pathogen will remain active and create a second wave infecting most of the population. We then show that when the duration of the encounters is considered, the spreading dynamics change significantly. Our results indicate that when considering airborne diseases, it might be crucial to consider the duration of temporal meetings to model the spread of pathogens in a population.

Contacts' temporal ordering and dynamics are crucial for understanding the transmission of infectious diseases. We introduce an interaction-driven model of an airborne disease over contact networks. We demonstrate our interaction-driven contagion model, instantiated for COVID-19, over history-maintaining random temporal networks and real-world contacts. We use it to evaluate temporal, spatiotemporal, and spatial social distancing policies. We find that a spatial distancing policy is mainly beneficial at the early stages of a disease. We then continue to evaluate temporal social distancing, that is, timeline dilation that maintains the activity potential. We expand our model to consider the exposure to viral load, which we correlate with meetings' duration. Using real-life contact data, we demonstrate the beneficial effect of timeline dilation on overall infection rates. Our results demonstrate that given the same transmission level, there is a decrease in the disease's infection rate and overall prevalence under timeline dilation conditions. We further show that slow-spreading pathogens (i.e., require more prolonged exposure to infect) spread roughly at the same rate as fast-spreading ones in highly active communities. This is surprising since slower pathogens follow paths that include longer meetings, while faster pathogens can potentially follow paths that include shorter meetings, which are more common. Our results demonstrate that the temporal dynamics of a community have a more significant effect on the spread of the disease than the characteristics of the spreading processes., Comment: Link to the open-source temporal random network library RandomDynamicGraph (RDG): https://github.com/ScanLab-ossi/DynamicRandomGraphs. Please cite this paper when using the code

Zipf's law defines an inverse proportion between a word's ranking in a given corpus and its frequency in it, roughly dividing the vocabulary into frequent words and infrequent ones. Here, we stipulate that within a domain an author's signature can be derived from, in loose terms, the author's missing popular words and frequently used infrequent-words. We devise a method, termed Latent Personal Analysis (LPA), for finding domain-based attributes for entities in a domain: their distance from the domain and their signature, which determines how they most differ from a domain. We identify the most suitable distance metric for the method among several and construct the distances and personal signatures for authors, the domain's entities. The signature consists of both over-used terms (compared to the average), and missing popular terms. We validate the correctness and power of the signatures in identifying users and set existence conditions. We then show uses for the method in explainable authorship attribution: we define algorithms that utilize LPA to identify two types of impersonation in social media: (1) authors with sockpuppets (multiple) accounts; (2) front users accounts, operated by several authors. We validate the algorithms and employ them over a large scale dataset obtained from a social media site with over 4000 users. We corroborate these results using temporal rate analysis. LPA can further be used to devise personal attributes in a wide range of scientific domains in which the constituents have a long-tail distribution of elements.

The temporal changes in complex systems of interactions have excited the research community in recent years as they encompass understandings on their dynamics and evolution. From the collective dynamics of organizations and online communities to the spreading of information and fake news, to name a few, temporal dynamics are fundamental in the understanding of complex systems. In this work, we quantify the level of engagement in dynamic complex systems of interactions, modeled as networks. We focus on interaction networks for which the dynamics of the interactions are coupled with that of the topology, such as online messaging, forums, and emails. We define two indices to capture the temporal level of engagement: the Temporal Network (edge) Intensity index, and the Temporal Dominance Inequality index. Our surprising results are that these measures are stationary for most measured networks, regardless of vast fluctuations in the size of the networks in time. Moreover, more than 80% of weekly changes in the indices values are bounded by less than 10%. The indices are stable between the temporal evolution of a network but are different between networks, and a classifier can determine the network the temporal indices belong to with high success. We find an exception in the Enron management email exchange during the year before its disintegration, in which both indices show high volatility throughout the inspected period., Comment: arXiv admin note: substantial text overlap with arXiv:1809.09613

Changes in the structure of observed social and complex networks' structure can indicate a significant underlying change in an organization, or reflect the response of the network to an external event. Automatic detection of change points in evolving networks is rudimentary to the research and the understanding of the effect of such events on networks. Here we present an easy-to-implement and fast framework for change point detection in temporal evolving networks. Unlike previous approaches, our method is size agnostic, and does not require either prior knowledge about the network's size and structure, nor does it require obtaining historical information or nodal identities over time. We use both synthetic data derived from dynamic models and two real datasets: Enron email exchange and Ask-Ubuntu forum. Our framework succeeds with both precision and recall and outperforms previous solutions

Modeling complex networks has been the focus of much research for over a decade. Preferential attachment (PA) is considered a common explanation to the self organization of evolving networks, suggesting that new nodes prefer to attach to more popular nodes. The PA model results in broad degree distributions, found in many networks, but cannot explain other common properties such as: The growth of nodes arriving late and Clustering (community structure). Here we show that when the tendency of networks to adhere to trends is incorporated into the PA model, it can produce networks with such properties. Namely, in trending networks, newly arriving nodes may become central at random, forming new clusters. In particular, we show that when the network is young it is more susceptible to trends, but even older networks may have trendy new nodes that become central in their structure. Alternatively, networks can be seen as composed of two parts: static, governed by a power law degree distribution, and a dynamic part governed by trends, as we show on Wiki pages. Our results also show that the arrival of trending new nodes not only creates new clusters, but also has an effect on the relative importance and centrality of all other nodes in the network. This can explain a variety of real world networks in economics, social and online networks, and cultural networks. Products popularity, formed by the network of people's opinions, exhibit these properties. Some lines of products are increasingly susceptible to trends and hence to shifts in popularity, while others are less trendy and hence more stable. We believe that our findings have a big impact on our understanding of real networks., Comment: 5 pages, 4 figures

The availability of end-hosts and their assigned routable IP addresses has impact on the ability to fight spammers and attackers, and on peer-to-peer application performance. Previous works study the availability of hosts mostly by using either active pinging or by studying access to a mail service, both approaches suffer from inherent inaccuracies. We take a different approach by measuring the IP addresses periodically reported by a uniquely identified group of the hosts running the DIMES agent. This fresh approach provides a chance to measure the true availability of end-hosts and the dynamics of their assigned routable IP addresses. Using a two month study of 1804 hosts, we find that over 60% of the hosts have a fixed IP address and 90% median availability, while some of the remaining hosts have more than 30 different IPs. For those that have periodically changing IP addresses, we find that the median average period per AS is roughly 24 hours, with a strong relation between the offline time and the probability of altering IP address.

Broadcast networks are often used in modern communication systems. A common broadcast network is a single hop shared media system, where a transmitted message is heard by all neighbors, such as some LAN networks. In this work we consider a more complex environment, in which a transmitted message is heard only by a group of neighbors, such as Ad-Hoc networks, satellite and radio networks, as well as wireless multistation backbone system for mobile communication. It is important to design efficient algorithms for such environments. Our main result is a new Leader Election algorithm, with O(n) time complexity and O(n*lg(n)) message transmission complexity. Our distributed solution uses a propagation of information with feedback (PIF) building block tuned to the broadcast media, and a special counting and joining approach for the election procedure phase. The latter is required for achieving the linear time. It is demonstrated that the broadcast model requires solutions which are different from the known point-to-point model., Comment: Apeared at the 12th International Symposium on DIStributed Computing, (DISC98)