Social Networks & Graphs 3

The session Social Networks & Graphs 3 will be held on wednesday, 2019-09-18, from 14:00 to 16:00, at room 0.002. The session chair is Michelangelo Ceci.


14:00 - 14:20
User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription (40)
Yu Shi (University of Illinois at Urbana-Champaign), Xinwei He (University of Illinois at Urbana-Champaign), Naijing Zhang (University of Illinois at Urbana-Champaign), Carl Yang (University of Illinois at Urbana-Champaign), Jiawei Han (University of Illinois at Urbana-Champaign)

Heterogeneous information networks (HINs) with rich semantics are ubiquitous in real-world applications. For a given HIN, many reasonable clustering results with distinct semantic meaning can simultaneously exist.User-guided clustering is hence of great practical value for HINs where users provide labels to a small portion of nodes.To cater to a broad spectrum of user guidance evidenced by different expected clustering results, carefully exploiting the signals residing in the data is potentially useful.Meanwhile, as one type of complex networks, HINs often encapsulate higher-order interactions that reflect the interlocked nature among nodes and edges.Network motifs, sometimes referred to as meta-graphs, have been used as tools to capture such higher-order interactions and reveal the many different semantics.We therefore approach the problem of user-guided clustering in HINs with network motifs.In this process, we identify the utility and importance of directly modeling higher-order interactions without collapsing them to pairwise interactions.To achieve this, we comprehensively transcribe the higher-order interaction signals to a series of tensors via motifs and propose the MoCHIN model based on joint non-negative tensor factorization.This approach applies to arbitrarily many, arbitrary forms of HIN motifs. An inference algorithm with speed-up methods is also proposed to tackle the challenge that tensor size grows exponentially as the number of nodes in a motif increases.We validate the effectiveness of the proposed method on two real-world datasets and three tasks, and MoCHIN outperforms all baselines in three evaluation tasks under three different metrics.Additional experiments demonstrated the utility of motifs and the benefit of directly modeling higher-order information especially when user guidance is limited.

Reproducible Research
14:20 - 14:40
Finding lasting dense graphs (J10)
Konstantinos Semertzidis, Evaggelia Pitoura, Evimaria Terzi, Panayiotis Tsaparas

15:00 - 15:20
Model-Free Inference of Diffusion Networks (J11)
Shoubo Hu, Bogdan Cautis, Zhitang Chen, Laiwan Chan, Yanhui Geng, Xiuqiang He

15:20 - 15:40
Counts-of-Counts Similarity for prediction and search in relational data (J12)
Manfred Jaeger, Marco Lippi, Giovanni Pellegrini, Andrea Passerini

15:40 - 16:00
Robust active attacks on social graphs (J13)
Sjouke Mauw, Yunior Ramírez-Cruz, Rolando Trujillo-Rasua

Parallel Sessions