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# In short
Short article considering HIN-based recommendation. The contribution consists in using not only meta-paths in the HIN, but also "enhanced" meta-paths which are related to more elaborate motifs (typically triangles)
# Summary
### 1. Introduction
- usually HIN-based RS rely on the number pf meta-paths from u to i, the more there are, the higher the recommendation
- Figure 1 summarizes situations that the authors want to distinguish: (u1,b4) and (u1,b3) are equivalent in terms of (P2) meta-paths, but not in terms of 3-nodes based "trust" patterns
- patterns here are considered using 3 nodes patterns (à la Milo et al) as represented in Figure 2, meta-paths based on these motifs are called Motif Enhanced Meta Path (MEMP)
- the concept is explored on 2 datasets: Epinions and CiaoDVD
### 2. Framework
- to compute similarity using MP, we build the adjacency matrix W_{Ai,Aj} where Ai and Aj are types, then we build the commuting matrix Cp which is a product of the adjacency matrices corresponding to path P
=> similarities are based on counting obtained by matrix products
##### MoHINRec framework
- they define adj matrix based on the same principle, but an edge is considered if it belongs to a pattern of interest (illustrated on Figure 3)
- equation 2: they merge different matrices (obtained with usual MPs or MEMPs) with an alpha-weighting
- then they test this with state-of-the-art HIN-based RS (actually, one they have designed in another work)
### 3. Experiments and analysis
- 2 datasets, described in Table 1:
- Epinions: ~22K users, 300K items, 900K ratings
- CiaoDVD: ~17K users, 16K items, 72K ratings
- evaluation metrics: MAE, RMSE (accuracy based)
- baselines for comparison: RegSVD (MF with regularization), SoReg (MF using social links for regularization), SocialMF (MF with social trust propagation), FMG (IN-based RS, state-of-the-art) - supposedly better than the others
- Settings: 8/1/1 train-validation-test, 5 experiment series with different splits
##### Performance comparison:
- Summary in Table 2: MoHINRec outperforms the others
- depending on the motif considered, we should vary the alpha coefficient to obtain the best possible performance (it is not clear for me how they tune alpha)
- performances vary depending on the dataset and morif considered
- FMG is still more efficient than othher benchmark methods
### 4. Related work
##### 4.1 HIN based recommendation
##### 4.2 motifs (very complex networks based: Milo et al., recent works by Leskovec et al.

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Although certain NP-complete problems stay hard even on average input,
some NP-complete problems are only hard in rare cases. Such NP-complete problems can provably be resolved very quickly on average case under some probability distributions over all possible inputs.
**1. What is the average case of a real-world graph ? **
**2. How to properly define a suitable probability distribution for real-world graphs ? **

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Summary:
The paper compares several supervised learning techniques to detect money laundering in bitcoins, using a company labelled dataset that they provide publicly. This is a tutorial presented at a KDD 2019 workshop: https://sites.google.com/view/kdd-adf-2019
Strengths:
I particularly loved the direct, clear and convincing writing style of the paper. Also, the authors (seem to) make their data publicly available, and it is a very interesting dataset (in particular thanks to the labelling).
Weaknesses:
The used data suffers from several limitations: it is very partial (approx 200 K transactions, while 400 M are available), and the labelling is not presented in details. More globally, several aspects of the work are not detailed, and many choices seem arbitrary. But the authors themselves state that this is "early experimental results" only, aimed at "inspiring others to work on this societally important challenge".

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Some "abstract" real-world graphs (such as social networks, WWW networks, call graphs, scientific collaboration graphs; and not road networks or power grid networks) empirically seem to have a small [hyperbolicity](https://en.wikipedia.org/wiki/Hyperbolic_metric_space).
This property can be used to approximate the input graph by a tree. The tree can, in turn, be used to approximate efficiently some quantities such as the distance between two nodes.
"The key point here is that in a hyperbolic graph selecting the root in the hyperbolic core ensures that it is already at the intersection of O(N) shortest paths"
Question: How can I select a root node efficiently?
Question: Is there any guaranty on the obtained approximation?
Question: How can I compute the hyperbolicity of a graph efficiently?
Not sure why there is "dynamic" in the title.

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