publications

2020

1. Weakly Supervised Attention for Hashtag Recommendation using Graph Data

   Javari, Amin, He, Zhankui, Huang, Zijie, Jeetu, Raj, and Chen-Chuan Chang, Kevin

   In Proceedings of The Web Conference 2020

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   Personalized hashtag recommendation for users could substantially promote user engagement in microblogging websites; users can discover microblogs aligned with their interests. However, user profiling on microblogging websites is challenging because most users tend not to generate content. Our core idea is to build a graph-based profile of users and incorporate it into hashtag recommendation. Indeed, user’s followee/follower links implicitly indicate their interests. Considering that microblogging networks are scale-free networks, to maintain the efficiency and effectiveness of the model, rather than analyzing the entire network, we model users based on their links towards hub nodes. That is, hashtags and hub nodes are projected into a shared latent space. To predict the relevance of a user to a hashtag, a projection of the user is built by aggregating the embeddings of her hub neighbors guided by an attention model and then compared with the hashtag. Classically, attention models can be trained in an end to end manner. However, due to the high complexity of our problem, we propose a novel weak supervision model for the attention component, which significantly improves the effectiveness of the model. We performed extensive experiments on two datasets collected from Twitter and Weibo, and the results confirm that our method substantially outperforms the baselines.

2. ROSE: Role-based Signed Network Embedding

   Javari, Amin, Derr, Tyler, Esmailian, Pouya, Tang, Jiliang, and Chang, Kevin Chen-Chuan

   In Proceedings of The Web Conference 2020

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   In real-world networks, nodes might have more than one type of relationship. Signed networks are an important class of such networks consisting of two types of relations: positive and negative. Recently, embedding signed networks has attracted increasing attention and is more challenging than classic networks since nodes are connected by paths with multi-types of links. Existing works capture the complex relationships by relying on social theories. However, this approach has major drawbacks, including the incompleteness/inaccurateness of such theories. Thus, we propose network transformation based embedding to address these shortcomings. The core idea is that rather than directly finding the similarities of two nodes from the complex paths connecting them, we can obtain their similarities through simple paths connecting their different roles. We employ this idea to build our proposed embedding technique that can be described in three steps: (1) the input directed signed network is transformed into an unsigned bipartite network with each node mapped to a set of nodes we denote as role-nodes. Each role-node captures a certain role that a node in the original network plays; (2) the network of role-nodes is embedded; and (3) the original network is encoded by aggregating the embedding vectors of role-nodes. Our experiments show the novel proposed technique substantially outperforms existing models.

3. Who will accept my request? Predicting response of link initiation in two-way relation networks

   Javari, Amin, Norouzitallab, Mehrab, and Jalili, Mahdi

   2020

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   Popularity of social networks has rapidly increased over the past few years, and daily lives interrupt without their proper functioning. Social networking platform provide multiple interaction types between individuals, such as creating and joining groups, sending and receiving messages, sharing interests and creating friendship relationships. This paper addresses an important problem in social networks analysis and mining that is how to predict link initiation feedback in two-way networks. Relationships between two individuals in a two-way network include a link invitation from one of the individuals, which will be an established link if it is accepted by the invitee. We consider a sport gaming social networking platform and construct a multilayer social network between a number of users. The network formed by the link initiation process is on one of the layers, while the other two layers include a messaging relationships and interactions between the users. We propose a methodology to solve the link initiation feedback prediction problem in this multilayer fashion. The proposed method is based on features extracted from meta-paths, i.e. paths defined between different individuals from multiples layers in multilayer networks. We proposed a cluster-based approach to handle the sparsity issue in the dataset. Experimental results show that the proposed method can provide accurate prediction that outperforms state-of-the-art methods.

2017

1. Statistical link label modeling for sign prediction: Smoothing sparsity by joining local and global information

   Javari, Amin, Qiu, HongXiang, Barzegaran, Elham, Jalili, Mahdi, and Chang, Kevin Chen-Chuan

   In 2017 IEEE International Conference on Data Mining (ICDM) 2017

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   One of the major issues in signed networks is to use network structure to predict the missing sign of an edge. In this paper, we introduce a novel probabilistic approach for the sign prediction problem. The main characteristic of the proposed models is their ability to adapt to the sparsity level of an input network. The sparsity of networks is one of the major reasons for the poor performance of many link prediction algorithms, in general, and sign prediction algorithms, in particular. Building a model that has an ability to adapt to the sparsity of the data has not yet been considered in the previous related works. We suggest that there exists a dilemma between local and global structures and attempt to build sparsity adaptive models by resolving this dilemma. To this end, we propose probabilistic prediction models based on local and global structures and integrate them based on the concept of smoothing. The model relies more on the global structures when the sparsity increases, whereas it gives more weights to the information obtained from local structures for low levels of the sparsity. The proposed model is assessed on three real-world signed networks, and the experiments reveal its consistent superiority over the state of the art methods. As compared to the previous methods, the proposed model not only better handles the sparsity problem, but also has lower computational complexity and can be updated using real-time data streams.

2016

1. Recommender systems for social networks analysis and mining: precision versus diversity

   Javari, Amin, Izadi, Malihe, and Jalili, Mahdi

   In Complex Systems and Networks 2016

2015

1. A time-aware recommender system based on dependency network of items

   Daneshmand, Seyed Mohammadhadi, Javari, Amin, Abtahi, Seyed Ebrahim, and Jalili, Mahdi

   The Computer Journal 2015
2. A probabilistic model to resolve diversity–accuracy challenge of recommendation systems

   Javari, Amin, and Jalili, Mahdi

   Knowledge and Information Systems 2015

2014

1. Unifying inconsistent evaluation metrics in recommender systems

   Izadi, Maliheh, Javari, Amin, and Jalilii, Mahdi

   In Proceedings RecSys Conference, REDD Workshop 2014
2. Enhancing precision of Markov-based recommenders using location information

   Abbasi, Ali, Javari, Amin, Jalili, Mahdi, and Rabiee, Hamid R

   In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) 2014
3. Recommender systems based on collaborative filtering and resource allocation

   Javari, Amin, Gharibshah, Joobin, and Jalili, Mahdi

   Social Network Analysis and Mining 2014
4. Accurate and novel recommendations: an algorithm based on popularity forecasting

   Javari, Amin, and Jalili, Mahdi

   ACM Transactions on Intelligent Systems and Technology (TIST) 2014
5. Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links

   Javari, Amin, and Jalili, Mahdi

   ACM Transactions on Intelligent Systems and Technology (TIST) 2014

2011

1. Nemesis 2011 Team Description Paper

   Norouzitallab, Mehrab, Javari, Amin, Mohajer, Milad, Ismael, Saman, and Salehizadeh, SMA

   2011