J. Leskovec’s scientific contributions

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Publications (1)

Fig. 1. (a) The size of the largest connected component of customers over time. The inset shows the linear growth in the number of customers n over time. the largest connected component contains less than 2.5% (100,420) of the nodes, and the second largest component has only 600 nodes. Still, some smaller communities , numbering in the tens of thousands of purchasers of DVDs in categories such as westerns, classics, and Japanese animated films (anime), had connected components spanning about 20% of their members. The insert in Figure 1 shows the growth of the customer base over time. Surprisingly it was linear, adding on average of 165,000 new users each month, which is an indication that the service itself was not spreading epidemically. Further evidence of nonviral spread is provided by the relatively high percentage (94%) of users who made their first recommendation without having previously received one.  
Fig. 3. Examples of two product recommendation networks: (a) First-aid study guide First Aid for the USMLE Step, (b) Japanese graphic novel (manga) Oh My Goddess!: Mara Strikes Back.  
Fig. 5. Distribution of the number of recommendations and number of purchases made by a customer.  
Fig. 6. Size distribution of cascades (size of cascade vs. count). The bold line presents a power fit.  
Fig. 7. Distribution of the number of exchanged recommendations between pairs of people.  

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The Dynamics of Viral Marketing
  • Article
  • Full-text available

January 2007

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2,466 Reads

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1,958 Citations

ACM Transactions on the Web

J. Leskovec

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L.A. Adamic

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Citations (1)

... As social media becomes increasingly integral to communication and marketing, understanding how content spreads and gains popularity is essential. Cascade popularity prediction is a fundamental technique for analyzing information propagation in social media applications such as Twitter, Facebook, and Weibo, and this predictive capability is crucial for a variety of applications, including curbing the spread of rumors [1,2], optimizing recommendation systems [3,4], shaping commercial marketing strategies [5] and relieving anxiety [6]. Therefore, accurate predictions can enhance communication strategies, improve crisis management, and mitigate the spread of misinformation. ...

Reference:

Multi-view temporal graph neural network for numerous miniature cascade popularity prediction
The Dynamics of Viral Marketing

ACM Transactions on the Web

J. Leskovec

·

L.A. Adamic

·