Danielle Cohen, Yoni Halpern, Noam Kahlon, Joel Oren, Omri Berkovitch, Sapir Caduri, Ido Dagan, Anatoly Efros. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Sampling random nodes is a fundamental algorithmic primitive in the analysis of massive networks, with many modern graph mining algorithms critically relying on it. We consider the task of generating a large collection of random nodes in the network assuming limited query access (where querying a node reveals its set of neighbors). In current approaches, based on long random walks, the number of queries per sample scales linearly with the mixing time of the network, which can be prohibitive for large real-world networks. We propose a new method for sampling multiple nodes that bypasses the dependence in the mixing time by explicitly searching for less accessible components in the network. We test our approach on a variety of real-world and synthetic networks with up to tens of millions of nodes, demonstrating a query complexity improvement of up to x20 compared to the state of the art.

Demographics of online users such as age and gender play an important role in personalized web applications, particularly in the News domain. However, it is difficult to directly obtain the demographic information of online users. Past works have attempted to predict user demography based on reading patterns obtained from news browsing data. However, such data can be very limited. Luckily, in recent years, posts and comments have become much prevalent among online users, and the comments from users of different demographics exhibit differences in contents and writing styles. Thus, comments can provide additional clues for demographic prediction. In this paper, we study predicting users' demographics based on both news browsing data and the associated user generated comments. To this end, we make a novel use of a recently introduced BERT-based model to embed each comment in the context of its associated article. We experiment on real-world datasets, and explore the contribution of both browsing data and user generated data in the task of predicting three different user attributes: gender, location type (e.g., rural vs. urban), and mobile device. Finally we show that our approach can effectively improve the performance of such predictions and outperforms baseline methods.

We study combinatorial problems with real world applications such as machine scheduling, routing, and assignment. We propose a method that combines Reinforcement Learning (RL) and planning. This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e.g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process. Our solution is quite generic, scalable, and leverages distributional knowledge of the problem parameters. We frame the solution process as an MDP, and take a Deep Q-Learning approach wherein states are represented as graphs, thereby allowing our trained policies to deal with arbitrary changes in a principled manner. Though learned policies work well in expectation, small deviations can have substantial negative effects in combinatorial settings. We mitigate these drawbacks by employing our graph-convolutional policies as non-optimal heuristics in a compatible search algorithm, Monte Carlo Tree Search, to significantly improve overall performance. We demonstrate our method on two problems: Machine Scheduling and Capacitated Vehicle Routing. We show that our method outperforms custom-tailored mathematical solvers, state of the art learning-based algorithms, and common heuristics, both in computation time and performance.

Summaries of fictional stories allow readers to quickly decide whether or not a story catches their interest. A major challenge in automatic summarization of fiction is the lack of standardized evaluation methodology or high-quality datasets for experimentation. In this work, we take a bottomup approach to this problem by assuming that story authors are uniquely qualified to inform such decisions. We collect a dataset of one million fiction stories with accompanying author-written summaries from Wattpad, an online story sharing platform. We identify commonly occurring summary components, of which a description of the main characters is the most frequent, and elicit descriptions of main characters directly from the authors for a sample of the stories. We propose two approaches to generate character descriptions, one based on ranking attributes found in the story text, the other based on classifying into a list of pre-defined attributes. We find that the classification-based approach performs the best in predicting character descriptions.

We study a game with \emph{strategic} vendors (the agents) who own multiple items and a single buyer with a submodular valuation function. The goal of the vendors is to maximize their revenue via pricing of the items, given that the buyer will buy the set of items that maximizes his net payoff.% (valuation minus the prices). We show this game may not always have a pure Nash equilibrium, in contrast to previous results for the special case where each vendor owns a single item. We do so by relating our game to an intermediate, discrete game in which the vendors only choose the available items, and their prices are set exogenously afterwards. We further make use of the intermediate game to provide tight bounds on the price of anarchy for the subset games that have pure Nash equilibria; we find that the optimal PoA reached in the previous special cases does not hold, but only a logarithmic one. Finally, we show that for a special case of submodular functions, efficient pure Nash equilibria always exist.

We consider the task of evaluating the spread of influence in large networks in the well-studied independent cascade model. We describe a novel sampling approach that can be used to design scalable algorithms with provable performance guarantees. These algorithms can be implemented in distributed computation frameworks such as MapReduce. We complement these results with a lower bound on the query complexity of influence estimation in this model. We validate the performance of these algorithms through experiments that demonstrate the efficacy of our methods and related heuristics.

We consider voting situations in which some candidates may turn out to be unavailable. When determining availability is costly (e.g., in terms of money, time, or computation), voting prior to determining candidate availability and testing the winner's availability after the vote may be beneficial. However, since few voting rules are robust to candidate deletion, winner determination requires a number of such availability tests. We outline a model for analyzing such problems, defining robust winners relative to potential candidate unavailability. We assess the complexity of computing robust winners for several voting rules. Assuming a distribution over availability, and costs for availability tests/queries, we describe algorithms for computing optimal query policies, which minimize the expected cost of determining true winners.

We consider a classic social choice problem in an online setting. In each round, a decision maker observes a single agent's preferences overa set of $m$ candidates, and must choose whether to irrevocably add a candidate to a selection set of limited cardinality $k$. Each agent's (positional) score depends on the candidates in the set when he arrives, and the decision-maker's goal is to maximize average (over all agents) score. We prove that no algorithm (even randomized) can achieve an approximationfactor better than $O(\frac{\log\log m}{\log m})$. In contrast, if the agents arrive in random order, we present a $(1 - \frac{1}{e} - o(1))$-approximatealgorithm, matching a lower bound for the off-line problem.We show that improved performance is possible for natural input distributionsor scoring rules. Finally, if the algorithm is permitted to revoke decisions at a fixedcost, we apply regret-minimization techniques to achieve approximation $1 - \frac{1}{e} - o(1)$ even for arbitrary inputs.

Vendors of all types face the problem of selecting a slate of product offerings—their assortment or catalog—that will maximize their profits. The profitability of a catalog is determined by both customer preferences and the offerings of their competitors. We develop a game-theoretic model for analyzing the vendor catalog optimization problem in the face of competing vendors. We show that computing a best response is intractable in general, but can be solved by dynamic programming given certain informational or structural assumptions about consumer preferences. We also analyze conditions under which pure Nash equilibria exist and provide several price of anarchy/stability results

Top-i voting is a common form of preference elicitation due to its conceptual simplicity both on the voters' side and on the decision maker's side. In a typical setting, given a set of candidates, the voters are required to submit only the k-length prefixes of their intrinsic rankings of the candidates. The decision maker then tries to correctly predict the winning candidate with respect to the complete preference profile according to a prescribed voting rule. This raises a tradeoff between the communication cost (given the specified value of k), and the ability to correctly predict the winner.

Motivated by applications to word-of-mouth advertising, we consider a game-theoretic scenario in which competing advertisers want to target initial adopters in a social network. Each advertiser wishes to maximize the resulting cascade of influence, modeled by a general network diffusion process. However, competition between products may adversely impact the rate of adoption for any given firm. The resulting framework gives rise to complex preferences that depend on the specifics of the stochastic diffusion model and the network topology.